Inferring age mixing from transmission clusters in a data missingess of Missing Completly at Random. This means during sampling, a missing individual is considered to be missing randomly (without any explanation or cause).

suppressMessages(library(dplyr))
suppressMessages(library(ggplot2))
suppressMessages(library(kableExtra))
# Function to summarised outputs 


# library(gmodels) # no more use of ci()

quant.med <- function(input){
  
  input <- na.omit(input)
  
  quantirles.v <- quantile(input, probs = seq(0, 1, 0.25))
  
  quantirles.25.50.75 <- as.numeric(quantirles.v)[2:4]
  
  return(quantirles.25.50.75)
  
}


min.max.sd.mean <- function(input){
  
  input <- na.omit(input)
  
  min.v <- min(input)
  max.v <- max(input)
  mean.v <- mean(input)
  sd.v <- sd(input)
  
  
  min.max.sd.mean.v <- c(min.v, max.v, mean.v, sd.v)
  
  return(min.max.sd.mean.v)
  
}


# Function for Average Root Mean Squared Error
ARMSE <- function(v1=v1, v2=v2) {
  d <- data.frame(v1,v2)
  r <- na.omit(d)
  y1 <- r[,1]
  y2 <- r[,2]
  error.na <- (y1-y2)/y2
  armse <- sqrt(mean(error.na^2)) 
  return(armse)}



# Function for Root Mean Squared Error
RMSE <- function(error) {
  error <- as.numeric(na.omit(error))
  rmse <- sqrt(mean(error^2)) 
  return(rmse)}

# Function for Mean Absolute Error
MAE <- function(error) { 
  error <- as.numeric(na.omit(error))
  mae <- mean(abs(error)) 
  return(mae)
}


# Function for Mean Realative Error
MRE <- function(error) { 
  error <- as.numeric(na.omit(error))
  mae <- mean(error) 
  return(mae)
}

# set_names(c("Centre", "Conforme", "Ne conforme pas", "Total admis", "% conforme")) %>% 
#                  flextable::flextable() %>% 
#                  flextable::autofit()
# dr2 <- read.csv("/home/david/age_mixing_patterns_phylogenetic/sim_outputs/F_results.mcarmar.large.AD_280_new_params_111.csv")
# dr3 <- read.csv("/home/david/age_mixing_patterns_phylogenetic/sim_outputs/F_results.mcarmar.large.AD_280_new_params_444.csv")
# dr4 <- read.csv("/home/david/age_mixing_patterns_phylogenetic/sim_outputs/F_results.mcarmar.large.AD_280_new_params_888.csv")
# dr1 <- read.csv("/home/david/age_mixing_patterns_phylogenetic/sim_outputs/F_results.mcarmar.large.AD_280_new_params_111777.csv")

# dr <- rbind(dr1, dr2, dr3, dr4)


# write.csv(dr, file = "/home/david/age_mixing_patterns_phylogenetic/sim_outputs/F_results.mcarmar.large.AD.csv")


dr <- read.csv("/home/david/age_mixing_patterns_phylogenetic/sim_outputs/F_results.mcarmar.large.AD.csv")

1 Epidemic characteristics

An HIV epidemic was simulated in an age and gender structured population. With an initial population of 10000 men and 10000 women, the simulation time was 40 years, and the infection was introduced in the population at the 10th year among randomly selected 10 individuals within 20 and 50 of age range. During simulation, demographic events like birth, and death were considered together with sexual partnerships. Refering to real world history of HIV, mainly ART treatment, we gradually allow interventions based on CD4 counts. With a seed sequence sampled in 1989, we assume it existed two years before (1987). It means that the simulation of started in 1977, and the infection was introduced in 1987 for 30 years in 2017. With same parameter combination, we performed 1120 simulations, and the statistics presented below will be minimum, median, mean, and maximum values of all 1120 simulations.

params <- c("formation.hazard.agegapry.baseline",
            "person.agegap.man.dist.normal.mu", 
            "person.agegap.woman.dist.normal.mu",
            "person.agegap.man.dist.normal.sigma",
            "person.agegap.woman.dist.normal.sigma")

vals <- c(2 , 10, 10, 5, 5)

description <- c("baseline value of age difference 
                 between a man and woman in relationship",
                 
                 "mean of preferred age differences
                 distribution for men",
                 
                 "mean of preferred age differences
                 distribution for women",
                 
                 "standard deviation of preferred age
                 differences distribution for men",
                 
                 "standard deviation of preferred age
                 differences distribution for women")


params.setup <- data.frame(params, vals, description)

colnames(params.setup) <- c("parameter associated to age mixing", "value", "description")

params.setup %>% 
  kable() %>% 
  kable_styling("striped") 
parameter associated to age mixing value description
formation.hazard.agegapry.baseline 2 baseline value of age difference between a man and woman in relationship
person.agegap.man.dist.normal.mu 10 mean of preferred age differences distribution for men
person.agegap.woman.dist.normal.mu 10 mean of preferred age differences distribution for women
person.agegap.man.dist.normal.sigma 5 standard deviation of preferred age differences distribution for men
person.agegap.woman.dist.normal.sigma 5 standard deviation of preferred age differences distribution for women
write.csv(params.setup, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_1_Model_Parameters.csv")

If we consider time point sampling at 40 simulation time, we can see that the epidemic is characterized by an increasing prevalence across early age groups in both men and women, with women carrying the most of the burden. After 40 years the trend reversed with a high prevalence among men and the lowest among women. The simulated epidemic looks like a typical sub-Saharan Africa epidemic.

1.1 Prevalence

The following table and figure show values of point prevalence at 40 simulation time among different age groups.

hiv.prev <- dr %>% 
  select(starts_with("prev.")) 

prev.m.15.24 <- quant.med(hiv.prev$prev.m.15.24)
prev.m.25.29 <- quant.med(hiv.prev$prev.m.25.29)
prev.m.30.34 <- quant.med(hiv.prev$prev.m.30.34)
prev.m.35.39 <- quant.med(hiv.prev$prev.m.35.39)
prev.m.40.44 <- quant.med(hiv.prev$prev.m.40.44)
prev.m.45.49 <- quant.med(hiv.prev$prev.m.45.49)

prev.w.15.24 <- quant.med(hiv.prev$prev.w.15.24)
prev.w.25.29 <- quant.med(hiv.prev$prev.w.25.29)
prev.w.30.34 <- quant.med(hiv.prev$prev.w.30.34)
prev.w.35.39 <- quant.med(hiv.prev$prev.w.35.39)
prev.w.40.44 <- quant.med(hiv.prev$prev.w.40.44)
prev.w.45.49 <- quant.med(hiv.prev$prev.w.45.49)

val.prev.F <- c(prev.m.15.24[2], prev.m.25.29[2], prev.m.30.34[2], prev.m.35.39[2], prev.m.40.44[2], prev.m.45.49[2], 
                prev.w.15.24[2], prev.w.25.29[2], prev.w.30.34[2], prev.w.35.39[2], prev.w.40.44[2], prev.w.45.49[2])

val.prev.L <- c(prev.m.15.24[1], prev.m.25.29[1], prev.m.30.34[1], prev.m.35.39[1], prev.m.40.44[1], prev.m.45.49[1], 
                prev.w.15.24[1], prev.w.25.29[1], prev.w.30.34[1], prev.w.35.39[1], prev.w.40.44[1], prev.w.45.49[1])

val.prev.U <- c(prev.m.15.24[3], prev.m.25.29[3], prev.m.30.34[3], prev.m.35.39[3], prev.m.40.44[3], prev.m.45.49[3], 
                prev.w.15.24[3], prev.w.25.29[3], prev.w.30.34[3], prev.w.35.39[3], prev.w.40.44[3], prev.w.45.49[3])


par.gender <- c(rep("Men", 6), rep("Women", 6))
agegroup <- rep(c("15-24", "25-29", "30-34", "35-39", "40-44", "45-49"), 2)

val.prev <- data.frame(agegroup, val.prev.L, val.prev.F, val.prev.U, par.gender)

val.prev.tab <- val.prev

names(val.prev.tab) <- c("age_group", "lower.Q1", "median", "upper.Q3", "gender")

val.prev.tab %>% 
  kable() %>% 
  kable_styling("striped") 
age_group lower.Q1 median upper.Q3 gender
15-24 0.0096192 0.0135247 0.0178232 Men
25-29 0.0321657 0.0402010 0.0497982 Men
30-34 0.0558920 0.0665703 0.0785671 Men
35-39 0.0698925 0.0858586 0.1019193 Men
40-44 0.0816493 0.0920245 0.1022722 Men
45-49 0.0716988 0.0807847 0.0907243 Men
15-24 0.0576923 0.0670897 0.0774699 Women
25-29 0.0952381 0.1079414 0.1227154 Women
30-34 0.1037026 0.1176434 0.1363636 Women
35-39 0.0853812 0.1040033 0.1269454 Women
40-44 0.0514376 0.0626058 0.0769911 Women
45-49 0.0407332 0.0510384 0.0632753 Women
write.csv(val.prev.tab, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_2_Prevalence.csv")
val.prev <- data.frame(agegroup, val.prev.L, val.prev.F, val.prev.U, par.gender)

names(val.prev) <- c("age_group", "lower.Q1", "median", "upper.Q3", "Gender")

plot.prev.men.women <- ggplot(val.prev, aes(x=age_group, y=median, colour=Gender, group = Gender)) + 
  geom_errorbar(aes(ymin=lower.Q1, ymax=upper.Q3), width=.1) +
  geom_line(size=.3) +
  geom_point() + 
  xlab("Age Groups") + ylab("HIV prevalence")


print(plot.prev.men.women)

ggsave(filename = "Plot_a_1_Prevalence.pdf",
       plot = plot.prev.men.women,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 16, height = 10, units = "cm")

1.2 Incidence

The age mixing in partnership and transmission will be studied within a time interval of 35 - 40 years of running time. The incidence among three age groups per gender is shown by the following figure. We can see that younger women had a lot of new cases compared to men in same age group, but men within 25 - 39, and 40 - 49 have much cases compared to women of same age groups.

hiv.inc <- dr %>% 
  select(starts_with("R.inc.")) 

inc.15.25.m <- quant.med(hiv.inc$R.inc.15.25.m)
inc.25.40.m <- quant.med(hiv.inc$R.inc.25.40.m)
inc.40.50.m <- quant.med(hiv.inc$R.inc.40.50.m)

inc.15.25.w <- quant.med(hiv.inc$R.inc.15.25.w)
inc.25.40.w <- quant.med(hiv.inc$R.inc.25.40.w)
inc.40.50.w <- quant.med(hiv.inc$R.inc.40.50.w)

Gender <- c(rep("Men", 3), rep("Women", 3))
age_group <- rep(c("15-24", "25-39", "40-49"), 2)

val.inc.F <- c(inc.15.25.m[2], inc.25.40.m[2], inc.40.50.m[2],
               inc.15.25.w[2], inc.25.40.w[2], inc.40.50.w[2])
val.inc.L <- c(inc.15.25.m[1], inc.25.40.m[1], inc.40.50.m[1],
               inc.15.25.w[1], inc.25.40.w[1], inc.40.50.w[1])
val.inc.U <- c(inc.15.25.m[3], inc.25.40.m[3], inc.40.50.m[3],
               inc.15.25.w[3], inc.25.40.w[3], inc.40.50.w[3])


val.inc <- data.frame(val.inc.L, val.inc.F, val.inc.U, Gender, age_group)

table.val.inc <- data.frame(age_group, val.inc.L, val.inc.F, val.inc.U, Gender)


names(table.val.inc) <- c("age_group", "lower.Q1", "median", "upper.Q3", "Gender")

table.val.inc %>% 
  kable() %>% 
  kable_styling("striped")
age_group lower.Q1 median upper.Q3 Gender
15-24 0.0020863 0.0028811 0.0039290 Men
25-39 0.0042933 0.0056458 0.0069273 Men
40-49 0.0015097 0.0019660 0.0025154 Men
15-24 0.0077611 0.0091388 0.0109045 Women
25-39 0.0004071 0.0006323 0.0009208 Women
40-49 0.0000000 0.0000000 0.0001357 Women
write.csv(table.val.inc, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_3_Incidence.csv")
plot.inc.men.women <- ggplot(val.inc, aes(x=age_group, y=val.inc.F, colour=Gender, group = Gender)) + 
  geom_errorbar(aes(ymin=val.inc.L, ymax=val.inc.U), width=.1) +
  geom_line(size=.3) +
  geom_point() + 
  xlab("Age Groups") + ylab("HIV incidence")

print(plot.inc.men.women)

ggsave(filename = "Plot_a_2_Incidence.pdf",
       plot = plot.inc.men.women,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 16, height = 10, units = "cm")

1.3 Sexual behaviour

In this simulation study, sexual behaviour of interest were mainly based on number of sexual partners and age preference which is reflected from age difference between sexual partners.

1.3.1 Point prevalence of concurrent partnerships

When we consider past 6 months (1/2) from simulation time 40, the point prevalence of concurrent partnership is:

prev.conc.p <- dr %>% 
  select(starts_with("R.p.prev.")) 

prev.conc.p.val <- quant.med(prev.conc.p$R.p.prev.6months.m)

prev.conc.p.val <- c(prev.conc.p.val[1], prev.conc.p.val[2], prev.conc.p.val[3])

names(prev.conc.p.val) <- c("lower.Q1", "med", "upper.Q3")

prev.conc.p.val %>% 
  kable() %>% 
  kable_styling("striped") 
x
lower.Q1 0.0157392
med 0.0167542
upper.Q3 0.0177862
write.csv(prev.conc.p.val, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_4_Prevalence_Concurrency.csv")

1.3.2 Age mixing in partnership

Age mixing patterns is defined as population-level patterns for how people choose partners with regards to age. If we consider past 5 years from simulation time 40, we have the following statistics of age mixing patterns in partnership.

1.3.2.1 True population level age mixing patterns in overall partnerships

In the fllowing analysis we depict popultion level age mixing patterns by computing different statistics based on age difference between man age and woman age.

  • AAD: average age difference across all relationships
  • VAD: variance of these age differences
  • SDAD: standard deviation of age differences
  • BSD: between-subject standard deviation of age differences
  • WSD: within-subject standard deviation of age differences
  • slope.male: slope from linear mixed effect model fitting
  • intercept.male: intercept from linear mixed effect model fitting

The following age mixing characteristics are computed for the entire population in partnership between 35 and 40 simulation time.

Note that we took the age of a man and substract the age of his partner (woman), hence we have suffix “.male” on these measurements.

dr.agemixstat <- dr %>% 
  select("R.AAD.male", "R.SDAD.male",  "R.slope.male",  "R.WSD.male", "R.BSD.male" , "R.intercept.male" )

pop.R.AAD.male <- round(quant.med(dr.agemixstat$R.AAD.male), digits = 3) 
pop.R.SDAD.male <- round(quant.med(dr.agemixstat$R.SDAD.male), digits = 3) 
pop.R.slope.male <- round(quant.med(dr.agemixstat$R.slope.male), digits = 3) 
pop.R.WSD.male <- round(quant.med(dr.agemixstat$R.WSD.male), digits = 3) 
pop.R.BSD.male <- round(quant.med(dr.agemixstat$R.BSD.male), digits = 3) 
pop.R.intercept.male <- round(quant.med(dr.agemixstat$R.intercept.male), digits = 3) 

age.mix.F <- c(pop.R.AAD.male[2], pop.R.SDAD.male[2], pop.R.BSD.male[2],
               pop.R.WSD.male[2], pop.R.slope.male[2], pop.R.intercept.male[2])

age.mix.L <- c(pop.R.AAD.male[1], pop.R.SDAD.male[1], pop.R.BSD.male[1],
               pop.R.WSD.male[1], pop.R.slope.male[1], pop.R.intercept.male[1])

age.mix.U <- c(pop.R.AAD.male[3], pop.R.SDAD.male[3], pop.R.BSD.male[3],
               pop.R.WSD.male[3], pop.R.slope.male[3], pop.R.intercept.male[3])

param.name <- c("AAD.male", "SDAD.male", "BSD.male" , "WSD.male", "slope.male",   "intercept.male" ) 

age.mixing.pop <- data.frame(param.name, age.mix.L, age.mix.F, age.mix.U)

colnames(age.mixing.pop) <- c("param", "lower.Q1", "med", "upper.Q3")

age.mixing.pop %>% 
  kable() %>% 
  kable_styling("striped") 
param lower.Q1 med upper.Q3
AAD.male 13.679 13.966 14.275
SDAD.male 6.042 6.223 6.402
BSD.male 2.213 2.313 2.392
WSD.male 1.730 1.797 1.870
slope.male 0.315 0.334 0.351
intercept.male -2.872 -2.596 -2.280
write.csv(age.mixing.pop, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_5_Age_Mixing_Population_Level.csv")

The results in the table above show that the median values of the average age difference (AAD) between men and women is around 14 years at population level which is almost 4 years far from mean of preferred age differences distribution for men and women. Its standard deviation (SDAD) is slightly more than 6 years, which is 1 year far from the value of standard deviation of preferred age differences distribution for men and women. We can see that the statistics of simulation study set up are reflecting here. [** is it correct, the assumption is that AAD ~ mu + sigma**]

1.3.2.2 True age mixing patterns in different sequence coverage scenarios of partnerships of infected and sampled invdividuals - MCAR

The table shows true median values of age mixing patterns (in relationships) of selected indiviuals (MCAR) in difference sequence coverage scenarios. These selecetd individuals are HIV positive, the measurement below assess their age mixing pattenrs within their sexual partnerships.

# MAR

d.MAR <- dr %>%
  select(contains("MAR.a."))


d.MAR.cov.35 <- d.MAR %>%
  select(contains("cov.MAR.a.35.")) 
d.MAR.cov.40 <- d.MAR %>%
  select(contains("cov.MAR.a.40.")) 
d.MAR.cov.45 <- d.MAR %>%
  select(contains("cov.MAR.a.45.")) 
d.MAR.cov.50 <- d.MAR %>%
  select(contains("cov.MAR.a.50.")) 
d.MAR.cov.55 <- d.MAR %>%
  select(contains("cov.MAR.a.55.")) 
d.MAR.cov.60 <- d.MAR %>%
  select(contains("cov.MAR.a.60.")) 
d.MAR.cov.65 <- d.MAR %>%
  select(contains("cov.MAR.a.65.")) 
d.MAR.cov.70 <- d.MAR %>%
  select(contains("cov.MAR.a.70.")) 
d.MAR.cov.75 <- d.MAR %>%
  select(contains("cov.MAR.a.75.")) 
d.MAR.cov.80 <- d.MAR %>%
  select(contains("cov.MAR.a.80.")) 
d.MAR.cov.85 <- d.MAR %>%
  select(contains("cov.MAR.a.85.")) 
d.MAR.cov.90 <- d.MAR %>%
  select(contains("cov.MAR.a.90.")) 
d.MAR.cov.95 <- d.MAR %>%
  select(contains("cov.MAR.a.95.")) 
d.MAR.cov.100 <- dr %>%
  select(contains("cov.MCAR.100.")) 


# Age mixing statistics in transmission networks --------------------------


# True age mix in different age mix scenarios

dr.trans.agemix.MAR.cov.35 <- d.MAR.cov.35 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.40 <- d.MAR.cov.40 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.45 <- d.MAR.cov.45 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.50 <- d.MAR.cov.50 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.55 <- d.MAR.cov.55 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.60 <- d.MAR.cov.60 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.65 <- d.MAR.cov.65 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.70 <- d.MAR.cov.70 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.75 <- d.MAR.cov.75 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.80 <- d.MAR.cov.80 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.85 <- d.MAR.cov.85 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.90 <- d.MAR.cov.90 %>% 
  select(contains(".T."))
dr.trans.agemix.MAR.cov.95 <- d.MAR.cov.95 %>% 
  select(contains(".T."))


# Age mixing in transmission 

T.35.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.35[,1]) 
T.35.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.35[,2])
T.35.slope.male <- quant.med(dr.trans.agemix.MAR.cov.35[,3])
T.35.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.35[,4])
T.35.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.35[,5])
T.35.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.35[,6])

T.40.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.40[,1]) 
T.40.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.40[,2])
T.40.slope.male <- quant.med(dr.trans.agemix.MAR.cov.40[,3])
T.40.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.40[,4])
T.40.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.40[,5])
T.40.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.40[,6])

T.45.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.45[,1]) 
T.45.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.45[,2])
T.45.slope.male <- quant.med(dr.trans.agemix.MAR.cov.45[,3])
T.45.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.45[,4])
T.45.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.45[,5])
T.45.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.45[,6])

T.50.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.50[,1]) 
T.50.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.50[,2])
T.50.slope.male <- quant.med(dr.trans.agemix.MAR.cov.50[,3])
T.50.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.50[,4])
T.50.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.50[,5])
T.50.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.50[,6])

T.55.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.55[,1]) 
T.55.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.55[,2])
T.55.slope.male <- quant.med(dr.trans.agemix.MAR.cov.55[,3])
T.55.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.55[,4])
T.55.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.55[,5])
T.55.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.55[,6])

T.60.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.60[,1]) 
T.60.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.60[,2])
T.60.slope.male <- quant.med(dr.trans.agemix.MAR.cov.60[,3])
T.60.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.60[,4])
T.60.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.60[,5])
T.60.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.60[,6])

T.65.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.65[,1]) 
T.65.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.65[,2])
T.65.slope.male <- quant.med(dr.trans.agemix.MAR.cov.65[,3])
T.65.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.65[,4])
T.65.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.65[,5])
T.65.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.65[,6])

T.70.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.70[,1]) 
T.70.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.70[,2])
T.70.slope.male <- quant.med(dr.trans.agemix.MAR.cov.70[,3])
T.70.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.70[,4])
T.70.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.70[,5])
T.70.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.70[,6])

T.75.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.75[,1]) 
T.75.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.75[,2])
T.75.slope.male <- quant.med(dr.trans.agemix.MAR.cov.75[,3])
T.75.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.75[,4])
T.75.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.75[,5])
T.75.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.75[,6])

T.80.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.80[,1]) 
T.80.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.80[,2])
T.80.slope.male <- quant.med(dr.trans.agemix.MAR.cov.80[,3])
T.80.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.80[,4])
T.80.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.80[,5])
T.80.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.80[,6])

T.85.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.85[,1]) 
T.85.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.85[,2])
T.85.slope.male <- quant.med(dr.trans.agemix.MAR.cov.85[,3])
T.85.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.85[,4])
T.85.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.85[,5])
T.85.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.85[,6])

T.90.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.90[,1]) 
T.90.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.90[,2])
T.90.slope.male <- quant.med(dr.trans.agemix.MAR.cov.90[,3])
T.90.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.90[,4])
T.90.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.90[,5])
T.90.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.90[,6])

T.95.AAD.male <- quant.med(dr.trans.agemix.MAR.cov.95[,1]) 
T.95.SDAD.male <- quant.med(dr.trans.agemix.MAR.cov.95[,2])
T.95.slope.male <- quant.med(dr.trans.agemix.MAR.cov.95[,3])
T.95.WSD.male <- quant.med(dr.trans.agemix.MAR.cov.95[,4])
T.95.BSD.male <- quant.med(dr.trans.agemix.MAR.cov.95[,5])
T.95.intercept.male <- quant.med(dr.trans.agemix.MAR.cov.95[,6])



age.mix.stats <- matrix(c(T.35.AAD.male[2], T.40.AAD.male[2], 
                          T.45.AAD.male[2], T.50.AAD.male[2], 
                          T.55.AAD.male[2], T.60.AAD.male[2], 
                          T.65.AAD.male[2], T.70.AAD.male[2], 
                          T.75.AAD.male[2], T.80.AAD.male[2], 
                          T.85.AAD.male[2], T.90.AAD.male[2], 
                          T.95.AAD.male[2], pop.R.AAD.male[2],
                          
                          T.35.SDAD.male[2], T.40.SDAD.male[2], 
                          T.45.SDAD.male[2], T.50.SDAD.male[2], 
                          T.55.SDAD.male[2], T.60.SDAD.male[2], 
                          T.65.SDAD.male[2], T.70.SDAD.male[2], 
                          T.75.SDAD.male[2], T.80.SDAD.male[2], 
                          T.85.SDAD.male[2], T.90.SDAD.male[2], 
                          T.95.SDAD.male[2], pop.R.SDAD.male[2],
                          
                          T.35.BSD.male[2], T.40.BSD.male[2], 
                          T.45.BSD.male[2], T.50.BSD.male[2], 
                          T.55.BSD.male[2], T.60.BSD.male[2], 
                          T.65.BSD.male[2], T.70.BSD.male[2], 
                          T.75.BSD.male[2], T.80.BSD.male[2], 
                          T.85.BSD.male[2], T.90.BSD.male[2], 
                          T.95.BSD.male[2], pop.R.BSD.male[2],
                          
                          T.35.WSD.male[2], T.40.WSD.male[2], 
                          T.45.WSD.male[2], T.50.WSD.male[2], 
                          T.55.WSD.male[2], T.60.WSD.male[2], 
                          T.65.WSD.male[2], T.70.WSD.male[2], 
                          T.75.WSD.male[2], T.80.WSD.male[2], 
                          T.85.WSD.male[2], T.90.WSD.male[2], 
                          T.95.WSD.male[2], pop.R.WSD.male[2],
                          
                          T.35.slope.male[2], T.40.slope.male[2], 
                          T.45.slope.male[2], T.50.slope.male[2], 
                          T.55.slope.male[2], T.60.slope.male[2], 
                          T.65.slope.male[2], T.70.slope.male[2], 
                          T.75.slope.male[2], T.80.slope.male[2], 
                          T.85.slope.male[2], T.90.slope.male[2], 
                          T.95.slope.male[2], pop.R.slope.male[2],
                          
                          T.35.intercept.male[2], T.40.intercept.male[2], 
                          T.45.intercept.male[2], T.50.intercept.male[2], 
                          T.55.intercept.male[2], T.60.intercept.male[2], 
                          T.65.intercept.male[2], T.70.intercept.male[2], 
                          T.75.intercept.male[2], T.80.intercept.male[2], 
                          T.85.intercept.male[2], T.90.intercept.male[2], 
                          T.95.intercept.male[2], pop.R.intercept.male[2]
),

ncol = 14,
byrow = TRUE)

age.mix.stats <- round(age.mix.stats, digits = 2)

colnames(age.mix.stats) <- c("cov.35", "cov.40", "cov.45",
                             "cov.50", "cov.55", "cov.60",
                             "cov.65", "cov.70", "cov.75",
                             "cov.80", "cov.85", "cov.90",
                             "cov.95", "true_100")

rownames(age.mix.stats) <- c("AAD.male", "SDAD.male", "BSD.male",  "WSD.male", "slope.male", "intercept.male") 


age.mix.stats %>% 
  kable() %>% 
  kable_styling("striped")
cov.35 cov.40 cov.45 cov.50 cov.55 cov.60 cov.65 cov.70 cov.75 cov.80 cov.85 cov.90 cov.95 true_100
AAD.male 9.87 9.82 9.92 10.03 10.05 10.20 10.27 10.39 10.46 10.54 10.70 10.80 10.91 13.97
SDAD.male 6.92 6.90 6.94 6.92 6.91 6.89 6.85 6.85 6.81 6.79 6.79 6.76 6.73 6.22
BSD.male 1.16 1.11 1.12 1.18 1.17 1.19 1.18 1.19 1.25 1.24 1.24 1.26 1.26 2.31
WSD.male 1.67 1.69 1.66 1.69 1.68 1.69 1.69 1.68 1.69 1.68 1.69 1.68 1.69 1.80
slope.male 0.14 0.15 0.14 0.15 0.15 0.15 0.15 0.16 0.16 0.16 0.16 0.17 0.17 0.33
intercept.male -0.12 -0.13 -0.10 -0.18 -0.19 -0.21 -0.21 -0.31 -0.33 -0.38 -0.41 -0.46 -0.53 -2.60
write.csv(age.mix.stats, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_6_Age_Mixing_Sampling_Coverages.csv")

The median value of AAD is one year apart from the true value at 100% sampling coverage (13.97), the SDAD still in same range (aroung 6) as at 100% sampling coverage.

Note that the increase in sampling (sequence) coverage does not have an obvious effect on statistics of age mixing patetrns of partnership of infected individuals. There is no statistics from low coverage which can be greater in magnitude than true values at 100% sampling coverage except for SDAD (values of SDAD at low sampling coverage can be greater compared to 100% sampling coverage).

1.3.2.3 Difference between true values of age mixing at population level and these from sampling scenarios in MCAR

When we compute the relative error between median of true values and these obtained in different sequence scenarios of age mixing statistics

\[\frac{(V_{true_{100}} – V_{true_{cov}})}{V_{true_{100}}}\]

This is the difference of true population level age mixing patterns in overall partnerships and true age mixing patterns in different sequence coverage scenarios of partnerships of infected and sampled invdividuals, which shows us how far are statistics of age mixing patterne in partnership for overall population and these of sub-population of infected individuals.

d.AAD.male <- (age.mix.stats[1,14] - age.mix.stats[1,])/age.mix.stats[1,14]
d.SDAD.male <- (age.mix.stats[2,14] - age.mix.stats[2,])/age.mix.stats[2,14]
d.slope.male <- (age.mix.stats[3,14] - age.mix.stats[3,])/age.mix.stats[3,14]
d.WSD.male <- (age.mix.stats[4,14] - age.mix.stats[4,])/age.mix.stats[4,14]
d.BSD.male <- (age.mix.stats[5,14] - age.mix.stats[5,])/age.mix.stats[5,14]
d.interc.male <- (age.mix.stats[6,14] - age.mix.stats[6,])/age.mix.stats[6,14]

diff.cov.true.age.mix.stats <- matrix(c(d.AAD.male, d.SDAD.male, d.BSD.male,
                                        d.WSD.male, d.slope.male, d.interc.male),
                                      ncol = 14,
                                      byrow = TRUE)

colnames(diff.cov.true.age.mix.stats) <- c("35", "40", "45",
                                           "50", "55", "60",
                                           "65", "70", "75",
                                           "80", "85", "90",
                                           "95", "true_100")

rownames(diff.cov.true.age.mix.stats) <- c("d.AAD.male", "d.SDAD.male", "d.BSD.male", "d.WSD.male", "d.slope.male",   "d.intercept.male") 



error.age.mix.AAD.male <- data.frame(x=paste(c(seq(from=35, to=95, by=5))),
                                     
                                     F = c(as.numeric(diff.cov.true.age.mix.stats[1,][-14])))
error.age.mix.AAD.male$parameter <- "AAD.male"

error.age.mix.SDAD.male <- data.frame(x=paste(c(seq(from=35, to=95, by=5))),
                                      
                                      F = c(as.numeric(diff.cov.true.age.mix.stats[2,][-14])))
error.age.mix.SDAD.male$parameter <- "SDAD.male"


error.age.mix.slope.male <- data.frame(x=paste(c(seq(from=35, to=95, by=5))),
                                       
                                       F = c(as.numeric(diff.cov.true.age.mix.stats[3,][-14])))
error.age.mix.slope.male$parameter <- "slope.male"

error.age.mix.WSD.male <- data.frame(x=paste(c(seq(from=35, to=95, by=5))),
                                     
                                     F = c(as.numeric(diff.cov.true.age.mix.stats[4,][-14])))
error.age.mix.WSD.male$parameter <- "WSD.male"


error.age.mix.BSD.male <- data.frame(x=paste(c(seq(from=35, to=95, by=5))),
                                     
                                     F = c(as.numeric(diff.cov.true.age.mix.stats[5,][-14])))
error.age.mix.BSD.male$parameter <- "BSD.male"


error.age.mix.intercept.male <- data.frame(x=paste(c(seq(from=35, to=95, by=5))),
                                           
                                           F = c(as.numeric(diff.cov.true.age.mix.stats[6,][-14])))
error.age.mix.intercept.male$parameter <- "intercept.male"


errors.age.mixing <- rbind(error.age.mix.AAD.male, error.age.mix.BSD.male,
                           error.age.mix.SDAD.male,error.age.mix.WSD.male,
                           error.age.mix.intercept.male, error.age.mix.slope.male)


# plots.errors.age.mix  <- ggplot(errors.age.mixing, aes(x=x, y=F, colour=parameter, group = parameter)) + 
#   # geom_errorbar(aes(ymin=L, ymax=U), width=.1) +
#   geom_line(size=1) +
#   geom_point() + 
#   xlab("Sampling Coverage") + ylab("Error") 
# 
# plots.errors.age.mix

colnames(errors.age.mixing) <- c("cov", "val", "Parameter")

plots.errors.age.mix  <- ggplot(errors.age.mixing, aes(x=cov, y=val, group = Parameter)) + 
  # geom_errorbar(aes(ymin=L, ymax=U), width=.1) +
  geom_line(aes(linetype=Parameter, color=Parameter))+ # ,  size=parameter
  geom_point(aes(color=Parameter))+ # ,  size=parameter
  scale_color_manual(values=c('#FF0000FF','#1A0808', '#00FF00', '#3CEFEF', '#1F4BC6', '#FF9900'))+
  # theme(legend.position="top")+
  xlab("Sampling Coverage (%)") + ylab("Error value for age mixing parameters") 


print(plots.errors.age.mix)

ggsave(filename = "Plot_a_3_error_age_mixing.pdf",
       plot = plots.errors.age.mix,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 26, height = 15, units = "cm")

The error for WSD.male is around 0.05 and SDAD.male being below zero at almost 0.05 in absolute value, AAD.male turns around 0.1, BSD.male and slope.male varies between 0.3 and 0.4 (at highest sampling coverage the slope being on top), and the intercept.male turns aroung 0.5. All the variations of these measurements across different sequence scenarios show no variation with the increase of sampling coverage.

Overall, in terms of error magnitude we can classify from the lowest to the highest: SDAD.male, WSD.male, AAD.male, BSD.male, slope.male, and intercept.male. And we can conclude that sampling coverage does not affect results.

2 Computing transmission clusters

HIV transmission clusters were identified in the phylogenetic trees based on high support for the grouping and low within cluster genetic distance with Cluster Picker software. The settings of Cluster Picker to compute transmission clusters were such that: the initial support threshold was set to 0.8 (used to split the tree into subtrees to reduce the number of computations, and it must be ≤ the main support threshold for clusters), the main support threshold for clusters was set to 0.7 (90% bootstrap support for clusters - this can be set to 0.7, 0.8 or 0.99 according to the literature), the genetic distance threshold for clusters was set to 0.045 (maximum 4.5 substitutions/site within clusters - this can be set to 0.015, or 0.03 according to the literature), and finally we set the option to output lists of clusters above a certain size which was 2.

2.1 Statistics of size of transmission clusters

Per each simulation for the existing 1120, we computed transmission cluster size statistics: mean, median, and standard deviation to have an idea on their size distribution. Here below, we present the median values of these statistics for overall 1120 simulations in different sampling coverage.

# Cluster sizes

dr.cl.size.MAR.cov.35 <- d.MAR.cov.35 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.40 <- d.MAR.cov.40 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.45 <- d.MAR.cov.45 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.50 <- d.MAR.cov.50 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.55 <- d.MAR.cov.55 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.60 <- d.MAR.cov.60 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.65 <- d.MAR.cov.65 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.70 <- d.MAR.cov.70 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.75 <- d.MAR.cov.75 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.80 <- d.MAR.cov.80 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.85 <- d.MAR.cov.85 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.90 <- d.MAR.cov.90 %>% 
  select(contains("cl.size"))
dr.cl.size.MAR.cov.95 <- d.MAR.cov.95 %>% 
  select(contains("cl.size"))

dr.cl.size.MAR.cov.100 <- d.MAR.cov.100 %>% 
  select(contains("cl.size"))


cl.size.mean.35 <- quant.med(dr.cl.size.MAR.cov.35$cov.MAR.a.35.mean.cl.size)
cl.size.med.35 <- quant.med(dr.cl.size.MAR.cov.35$cov.MAR.a.35.med.cl.size)
cl.size.sd.35 <- quant.med(dr.cl.size.MAR.cov.35$cov.MAR.a.35.sd.cl.size)

cl.size.mean.40 <- quant.med(dr.cl.size.MAR.cov.40$cov.MAR.a.40.mean.cl.size)
cl.size.med.40 <- quant.med(dr.cl.size.MAR.cov.40$cov.MAR.a.40.med.cl.size)
cl.size.sd.40 <- quant.med(dr.cl.size.MAR.cov.40$cov.MAR.a.40.sd.cl.size)

cl.size.mean.45 <- quant.med(dr.cl.size.MAR.cov.45$cov.MAR.a.45.mean.cl.size)
cl.size.med.45 <- quant.med(dr.cl.size.MAR.cov.45$cov.MAR.a.45.med.cl.size)
cl.size.sd.45 <- quant.med(dr.cl.size.MAR.cov.45$cov.MAR.a.45.sd.cl.size)

cl.size.mean.50 <- quant.med(dr.cl.size.MAR.cov.50$cov.MAR.a.50.mean.cl.size)
cl.size.med.50 <- quant.med(dr.cl.size.MAR.cov.50$cov.MAR.a.50.med.cl.size)
cl.size.sd.50 <- quant.med(dr.cl.size.MAR.cov.50$cov.MAR.a.50.sd.cl.size)

cl.size.mean.55 <- quant.med(dr.cl.size.MAR.cov.55$cov.MAR.a.55.mean.cl.size)
cl.size.med.55 <- quant.med(dr.cl.size.MAR.cov.55$cov.MAR.a.55.med.cl.size)
cl.size.sd.55 <- quant.med(dr.cl.size.MAR.cov.55$cov.MAR.a.55.sd.cl.size)

cl.size.mean.60 <- quant.med(dr.cl.size.MAR.cov.60$cov.MAR.a.60.mean.cl.size)
cl.size.med.60 <- quant.med(dr.cl.size.MAR.cov.60$cov.MAR.a.60.med.cl.size)
cl.size.sd.60 <- quant.med(dr.cl.size.MAR.cov.60$cov.MAR.a.60.sd.cl.size)

cl.size.mean.65 <- quant.med(dr.cl.size.MAR.cov.65$cov.MAR.a.65.mean.cl.size)
cl.size.med.65 <- quant.med(dr.cl.size.MAR.cov.65$cov.MAR.a.65.med.cl.size)
cl.size.sd.65 <- quant.med(dr.cl.size.MAR.cov.65$cov.MAR.a.65.sd.cl.size)

cl.size.mean.70 <- quant.med(dr.cl.size.MAR.cov.70$cov.MAR.a.70.mean.cl.size)
cl.size.med.70 <- quant.med(dr.cl.size.MAR.cov.70$cov.MAR.a.70.med.cl.size)
cl.size.sd.70 <- quant.med(dr.cl.size.MAR.cov.70$cov.MAR.a.70.sd.cl.size)

cl.size.mean.75 <- quant.med(dr.cl.size.MAR.cov.75$cov.MAR.a.75.mean.cl.size)
cl.size.med.75 <- quant.med(dr.cl.size.MAR.cov.75$cov.MAR.a.75.med.cl.size)
cl.size.sd.75 <- quant.med(dr.cl.size.MAR.cov.75$cov.MAR.a.75.sd.cl.size)

cl.size.mean.80 <- quant.med(dr.cl.size.MAR.cov.80$cov.MAR.a.80.mean.cl.size)
cl.size.med.80 <- quant.med(dr.cl.size.MAR.cov.80$cov.MAR.a.80.med.cl.size)
cl.size.sd.80 <- quant.med(dr.cl.size.MAR.cov.80$cov.MAR.a.80.sd.cl.size)

cl.size.mean.85 <- quant.med(dr.cl.size.MAR.cov.85$cov.MAR.a.85.mean.cl.size)
cl.size.med.85 <- quant.med(dr.cl.size.MAR.cov.85$cov.MAR.a.85.med.cl.size)
cl.size.sd.85 <- quant.med(dr.cl.size.MAR.cov.85$cov.MAR.a.85.sd.cl.size)

cl.size.mean.90 <- quant.med(dr.cl.size.MAR.cov.90$cov.MAR.a.90.mean.cl.size)
cl.size.med.90 <- quant.med(dr.cl.size.MAR.cov.90$cov.MAR.a.90.med.cl.size)
cl.size.sd.90 <- quant.med(dr.cl.size.MAR.cov.90$cov.MAR.a.90.sd.cl.size)

cl.size.mean.95 <- quant.med(dr.cl.size.MAR.cov.95$cov.MAR.a.95.mean.cl.size)
cl.size.med.95 <- quant.med(dr.cl.size.MAR.cov.95$cov.MAR.a.95.med.cl.size)
cl.size.sd.95 <- quant.med(dr.cl.size.MAR.cov.95$cov.MAR.a.95.sd.cl.size)


cl.size.mean.100 <- quant.med(dr.cl.size.MAR.cov.100$cov.MCAR.100.mean.cl.size)
cl.size.med.100 <- quant.med(dr.cl.size.MAR.cov.100$cov.MCAR.100.med.cl.size)
cl.size.sd.100 <- quant.med(dr.cl.size.MAR.cov.100$cov.MCAR.100.sd.cl.size)


cl.size.mean.df <- data.frame(x=c(paste(c(seq(from=35, to=95, by=5))), "true_100"),
                              
                              F = c(cl.size.mean.35[2], cl.size.mean.40[2], 
                                    cl.size.mean.45[2], cl.size.mean.50[2], 
                                    cl.size.mean.55[2], cl.size.mean.60[2], 
                                    cl.size.mean.65[2], cl.size.mean.70[2], 
                                    cl.size.mean.75[2], cl.size.mean.80[2], 
                                    cl.size.mean.85[2], cl.size.mean.90[2], 
                                    cl.size.mean.95[2], cl.size.mean.100[2]),
                              
                              L = c(cl.size.mean.35[1], cl.size.mean.40[1], 
                                    cl.size.mean.45[1], cl.size.mean.50[1], 
                                    cl.size.mean.55[1], cl.size.mean.60[1], 
                                    cl.size.mean.65[1], cl.size.mean.70[1], 
                                    cl.size.mean.75[1], cl.size.mean.80[1], 
                                    cl.size.mean.85[1], cl.size.mean.90[1], 
                                    cl.size.mean.95[1], cl.size.mean.100[1]),
                              
                              U = c(cl.size.mean.35[3], cl.size.mean.40[3], 
                                    cl.size.mean.45[3], cl.size.mean.50[3], 
                                    cl.size.mean.55[3], cl.size.mean.60[3], 
                                    cl.size.mean.65[3], cl.size.mean.70[3], 
                                    cl.size.mean.75[3], cl.size.mean.80[3], 
                                    cl.size.mean.85[3], cl.size.mean.90[3], 
                                    cl.size.mean.95[3], cl.size.mean.100[3]))

cl.size.mean.df$parameter <- "Mean"


cl.size.med.df <- data.frame(x=c(paste(c(seq(from=35, to=95, by=5))), "true_100"),
                             
                             F = c(cl.size.med.35[2], cl.size.med.40[2], 
                                   cl.size.med.45[2], cl.size.med.50[2], 
                                   cl.size.med.55[2], cl.size.med.60[2], 
                                   cl.size.med.65[2], cl.size.med.70[2], 
                                   cl.size.med.75[2], cl.size.med.80[2], 
                                   cl.size.med.85[2], cl.size.med.90[2], 
                                   cl.size.med.95[2], cl.size.med.100[2]),
                             
                             L = c(cl.size.med.35[1], cl.size.med.40[1], 
                                   cl.size.med.45[1], cl.size.med.50[1], 
                                   cl.size.med.55[1], cl.size.med.60[1], 
                                   cl.size.med.65[1], cl.size.med.70[1], 
                                   cl.size.med.75[1], cl.size.med.80[1], 
                                   cl.size.med.85[1], cl.size.med.90[1], 
                                   cl.size.med.95[1], cl.size.med.100[1]),
                             
                             U = c(cl.size.med.35[3], cl.size.med.40[3], 
                                   cl.size.med.45[3], cl.size.med.50[3], 
                                   cl.size.med.55[3], cl.size.med.60[3], 
                                   cl.size.med.65[3], cl.size.med.70[3], 
                                   cl.size.med.75[3], cl.size.med.80[3], 
                                   cl.size.med.85[3], cl.size.med.90[3], 
                                   cl.size.med.95[3], cl.size.med.100[3]))

cl.size.med.df$parameter <- "Median"


cl.size.sd.df <- data.frame(x=c(paste(c(seq(from=35, to=95, by=5))), "true_100"),
                            
                            F = c(cl.size.sd.35[2], cl.size.sd.40[2], 
                                  cl.size.sd.45[2], cl.size.sd.50[2], 
                                  cl.size.sd.55[2], cl.size.sd.60[2], 
                                  cl.size.sd.65[2], cl.size.sd.70[2], 
                                  cl.size.sd.75[2], cl.size.sd.80[2], 
                                  cl.size.sd.85[2], cl.size.sd.90[2], 
                                  cl.size.sd.95[2], cl.size.sd.100[2]),
                            
                            L = c(cl.size.sd.35[1], cl.size.sd.40[1], 
                                  cl.size.sd.45[1], cl.size.sd.50[1], 
                                  cl.size.sd.55[1], cl.size.sd.60[1], 
                                  cl.size.sd.65[1], cl.size.sd.70[1], 
                                  cl.size.sd.75[1], cl.size.sd.80[1], 
                                  cl.size.sd.85[1], cl.size.sd.90[1], 
                                  cl.size.sd.95[1], cl.size.sd.100[1]),
                            
                            U = c(cl.size.sd.35[3], cl.size.sd.40[3], 
                                  cl.size.sd.45[3], cl.size.sd.50[3], 
                                  cl.size.sd.55[3], cl.size.sd.60[3], 
                                  cl.size.sd.65[3], cl.size.sd.70[3], 
                                  cl.size.sd.75[3], cl.size.sd.80[3], 
                                  cl.size.sd.85[3], cl.size.sd.90[3], 
                                  cl.size.sd.95[3], cl.size.sd.100[3]))

cl.size.sd.df$parameter <- "Standard_Dev"


# cl.number_phylo_pairings.df <- data.frame(x=c(seq(from=35, to=95, by=5), "cov.100"),
#                             
#                             F = c(cl.number_phylo_pairings.35[2], cl.number_phylo_pairings.40[2], 
#                                   cl.number_phylo_pairings.45[2], cl.number_phylo_pairings.50[2], 
#                                   cl.number_phylo_pairings.55[2], cl.number_phylo_pairings.60[2], 
#                                   cl.number_phylo_pairings.65[2], cl.number_phylo_pairings.70[2], 
#                                   cl.number_phylo_pairings.75[2], cl.number_phylo_pairings.80[2], 
#                                   cl.number_phylo_pairings.85[2], cl.number_phylo_pairings.90[2], 
#                                   cl.number_phylo_pairings.95[2], cl.number_phylo_pairings.100[2]),
#                             
#                             L = c(cl.number_phylo_pairings.35[1], cl.number_phylo_pairings.40[1], 
#                                   cl.number_phylo_pairings.45[1], cl.number_phylo_pairings.50[1], 
#                                   cl.number_phylo_pairings.55[1], cl.number_phylo_pairings.60[1], 
#                                   cl.number_phylo_pairings.65[1], cl.number_phylo_pairings.70[1], 
#                                   cl.number_phylo_pairings.75[1], cl.number_phylo_pairings.80[1], 
#                                   cl.number_phylo_pairings.85[1], cl.number_phylo_pairings.90[1], 
#                                   cl.number_phylo_pairings.95[1], cl.number_phylo_pairings.100[1]),
#                             
#                             U = c(cl.number_phylo_pairings.35[3], cl.number_phylo_pairings.40[3], 
#                                   cl.number_phylo_pairings.45[3], cl.number_phylo_pairings.50[3], 
#                                   cl.number_phylo_pairings.55[3], cl.number_phylo_pairings.60[3], 
#                                   cl.number_phylo_pairings.65[3], cl.number_phylo_pairings.70[3], 
#                                   cl.number_phylo_pairings.75[3], cl.number_phylo_pairings.80[3], 
#                                   cl.number_phylo_pairings.85[3], cl.number_phylo_pairings.90[3], 
#                                   cl.number_phylo_pairings.95[3], cl.number_phylo_pairings.100[3]))
# 
# cl.number_phylo_pairings.df$param <- "pairings"



clust.size.stats <- rbind(cl.size.mean.df, cl.size.med.df, cl.size.sd.df) # , cl.number_phylo_pairings.df)
clust.size.stats$parameter <- factor(clust.size.stats$parameter)

2.1.1 Number of pairings in transmission clusters

# pairings.clust.size.stats <- dplyr::filter(clust.size.stats, clust.size.stats$param=="pairings")
# 
# pairings.clust <- pairings.clust.size.stats %>% transmute(seq.cov = x, lower.Q1 = L, med = F, upper.Q3 = U)
# 
# pairings.clust %>% 
#   kable() %>% 
#   kable_styling("striped") 
# 
# 
# 
# plot.pairings.clust <- ggplot(pairings.clust, aes(x=seq.cov, y=med)) + 
#   geom_errorbar(aes(ymin=lower.Q1, ymax=upper.Q3), width=.1) +
#   geom_line(size=.3) +
#   geom_point() + 
#   xlab("Sampling Coverage") + ylab("Number of pairings")
# 
# 
# plot.pairings.clust

2.1.2 Mean of transmission cluster size

mean.clust.size.stats <- dplyr::filter(clust.size.stats, clust.size.stats$parameter=="Mean")

mean.clust.size.stats <- mean.clust.size.stats %>% transmute(seq.cov = x, lower.Q1 = L, med = F, upper.Q3 = U)

mean.clust.size.stats %>% 
  kable() %>% 
  kable_styling("striped") 
seq.cov lower.Q1 med upper.Q3
35 2.615385 2.857143 3.214286
40 2.666667 2.916667 3.222222
45 2.714286 3.000000 3.333333
50 2.764706 3.074176 3.384615
55 2.823529 3.100000 3.414087
60 2.857143 3.187500 3.526316
65 2.920769 3.229021 3.523810
70 2.937500 3.246753 3.578947
75 2.977273 3.272727 3.636364
80 3.031061 3.318182 3.666667
85 3.054805 3.354839 3.672619
90 3.098171 3.388889 3.737681
95 3.123087 3.444444 3.827211
true_100 3.416667 3.740741 4.108747
write.csv(mean.clust.size.stats, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_7_Mean_Clusters_Size.csv")


plot.mean.clust.size.stats <- ggplot(mean.clust.size.stats, aes(x=seq.cov, y=med)) + 
  geom_errorbar(aes(ymin=lower.Q1, ymax=upper.Q3), width=.1) +
  geom_line(size=.3) +
  geom_point() + 
  xlab("Sampling Coverage (%)") + ylab("Mean Cluster Size")


print(plot.mean.clust.size.stats)
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

ggsave(filename = "Plot_a_4_Mean_Clusters_Size.pdf",
       plot = plot.mean.clust.size.stats,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 16, height = 10, units = "cm")
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

2.1.3 Median of transmission cluster size

med.clust.size.stats <- dplyr::filter(clust.size.stats, clust.size.stats$parameter=="Median")

med.clust.size.stats <- med.clust.size.stats %>% transmute(seq.cov = x, lower.Q1 = L, med = F, upper.Q3 = U)

med.clust.size.stats %>% 
  kable() %>% 
  kable_styling("striped") 
seq.cov lower.Q1 med upper.Q3
35 2 2 2.5
40 2 2 2.5
45 2 2 2.5
50 2 2 2.5
55 2 2 2.5
60 2 2 3.0
65 2 2 2.5
70 2 2 2.5
75 2 2 3.0
80 2 2 3.0
85 2 2 3.0
90 2 2 3.0
95 2 2 3.0
true_100 2 2 3.0
write.csv(med.clust.size.stats, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_8_Median_Clusters_Size.csv")


plot.med.clust.size.stats <- ggplot(med.clust.size.stats, aes(x=seq.cov, y=med)) + 
  geom_errorbar(aes(ymin=lower.Q1, ymax=upper.Q3), width=.1) +
  geom_line(size=.3) +
  geom_point() + 
  xlab("Sampling Coverage (%)") + ylab("Median Cluster Size")


print(plot.med.clust.size.stats)
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

ggsave(filename = "Plot_a_5_Median_Clusters_Size.pdf",
       plot = plot.med.clust.size.stats,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 16, height = 10, units = "cm")
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

2.1.4 Standard deviation of transmission cluster size

sd.clust.size.stats <- dplyr::filter(clust.size.stats, clust.size.stats$parameter=="Standard_Dev")

sd.clust.size.stats <- sd.clust.size.stats %>% transmute(seq.cov = x, lower.Q1 = L, med = F, upper.Q3 = U)

sd.clust.size.stats %>% 
  kable() %>% 
  kable_styling("striped") 
seq.cov lower.Q1 med upper.Q3
35 0.9607689 1.337493 1.843909
40 1.0328593 1.460092 1.959763
45 1.1547005 1.621903 2.182701
50 1.2754213 1.691392 2.290485
55 1.3480318 1.792830 2.405883
60 1.3988245 1.918734 2.580731
65 1.5394054 2.027135 2.595767
70 1.5895492 2.114166 2.717395
75 1.6269784 2.122601 2.814619
80 1.7015583 2.252708 2.852964
85 1.7842516 2.267787 3.008034
90 1.8405084 2.339130 3.091086
95 1.9004222 2.475356 3.184115
true_100 2.5001953 3.128754 3.995274
write.csv(sd.clust.size.stats, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_9_Standard_Dev_Clusters_Size.csv")



plot.sd.clust.size.stats <- ggplot(sd.clust.size.stats, aes(x=seq.cov, y=med)) + 
  geom_errorbar(aes(ymin=lower.Q1, ymax=upper.Q3), width=.1) +
  geom_line(size=.3) +
  geom_point() + 
  xlab("Sampling Coverage (%)") + ylab("Stndard deviation Cluster Size")



print(plot.sd.clust.size.stats)
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

ggsave(filename = "Plot_a_6_Standard_Dev_Clusters_Size.pdf",
       plot = plot.sd.clust.size.stats,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 16, height = 10, units = "cm")
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

The mean and median of transmission cluster sizes increase with the sequence coverage, which is not the case for the standard deviation which remain almost constant with wide error bars.

# plot.clust.size.stats <- ggplot(clust.size.stats, aes(x=x, y=F, colour=param, group = param)) + 
#   geom_errorbar(aes(ymin=L, ymax=U), width=.1) +
#   geom_line(size=.3) +
#   geom_point() + 
#   xlab("Sampling Coverage") + ylab("Value")
# # ggtitle("Statistics  of transmission clusters")

colnames(clust.size.stats) <- c("cov", "F", "L", "U", "Parameter")

plot.clust.size.stats  <- ggplot(clust.size.stats, aes(x=cov, y=F, group = Parameter)) + # ggplot(clust.size.stats[-c(pairings)], aes(x=x, y=F, group = param)) + 
  # geom_errorbar(aes(ymin=L, ymax=U), width=.1) +
  geom_line(aes(linetype=Parameter, color=Parameter))+ # ,  size=parameter
  geom_point(aes(color=Parameter))+ # ,  size=parameter
  scale_color_manual(values=c('#FF0000FF','#1A0808', '#00FF00', '#3CEFEF'))+ # , '#1F4BC6', '#FF9900
  # theme(legend.position="top")+
  xlab("Sampling Coverage (%)") + ylab("Value") 



print(plot.clust.size.stats)

ggsave(filename = "Plot_a_7_Clusters_Size_Stats.pdf",
       plot = plot.clust.size.stats,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 16, height = 10, units = "cm")

3 Number of pairings in transmission clusters

We computed plausible pairings between men and women from the transmission clusters. In other words, we estimated plausible transmission network from a phylogenetictree. To build that plausible transmission network from the phylogenetic tree, we computed first the time to the most recent ancestor matrix (tMRCA), which is a contigency matrix. Thereafter, we filtered this matrix by gender, transmission cluster IDs, and a threshold value of tMRCA (7 years). This means that we have a pairing between individuals \(x_i\) and \(x_j\) if they are within same transmission cluster, have different gender, and their tMRCA does not exceed 7 years.

Since the simlation was performed 1120 times, the number of pairings presented in the tables below are median values.

We have in the following sections: true pairings which are computed from the transmission network from partnership and transmission records, and pairings estimated from phylogenetic tree.

3.1 True number of pairings at 100% sampling (sequence) coverage

Within 35 and 40 simulation time, if we consider the true pairings of all those who were HIV positive, we have the following table:

dr.cov.100 <- dr %>% 
  select(contains(".100"))

M.15.25.F.15.25.cov.100 <- quant.med(dr.cov.100$cov.MCAR.100.cl.true.M.15.25.F.15.25) # quant.med(dr.cov.100$cov.100.M.15.25.F.15.25)
M.25.40.F.15.25.cov.100 <- quant.med(dr.cov.100$cov.MCAR.100.cl.true.M.25.40.F.15.25) # quant.med(dr.cov.100$cov.100.M.25.40.F.15.25)
M.40.50.F.15.25.cov.100 <- quant.med(dr.cov.100$cov.MCAR.100.cl.true.M.40.50.F.15.25) # quant.med(dr.cov.100$cov.100.M.40.50.F.15.25)

M.15.25.F.25.40.cov.100 <- quant.med(dr.cov.100$cov.MCAR.100.cl.true.M.15.25.F.25.40) # quant.med(dr.cov.100$cov.100.M.15.25.F.25.40)
M.25.40.F.25.40.cov.100 <- quant.med(dr.cov.100$cov.MCAR.100.cl.true.M.25.40.F.25.40) # quant.med(dr.cov.100$cov.100.M.25.40.F.25.40)
M.40.50.F.25.40.cov.100 <- quant.med(dr.cov.100$cov.MCAR.100.cl.true.M.40.50.F.25.40) # quant.med(dr.cov.100$cov.100.M.40.50.F.25.40)

M.15.25.F.40.50.cov.100 <- quant.med(dr.cov.100$cov.MCAR.100.cl.true.M.15.25.F.40.50) # quant.med(dr.cov.100$cov.100.M.15.25.F.40.50)
M.25.40.F.40.50.cov.100 <- quant.med(dr.cov.100$cov.MCAR.100.cl.true.M.25.40.F.40.50) # quant.med(dr.cov.100$cov.100.M.25.40.F.40.50)
M.40.50.F.40.50.cov.100 <- quant.med(dr.cov.100$cov.MCAR.100.cl.true.M.40.50.F.40.50) # quant.med(dr.cov.100$cov.100.M.40.50.F.40.50)



true.cov.100.age.groups.table <- matrix(c((M.15.25.F.15.25.cov.100[2]), (M.15.25.F.25.40.cov.100[2]), (M.15.25.F.40.50.cov.100[2]),
                                          (M.25.40.F.15.25.cov.100[2]), (M.25.40.F.25.40.cov.100[2]), (M.25.40.F.40.50.cov.100[2]),
                                          (M.40.50.F.15.25.cov.100[2]), (M.40.50.F.25.40.cov.100[2]), (M.40.50.F.40.50.cov.100[2])),
                                        ncol = 3,
                                        byrow = TRUE)

colnames(true.cov.100.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
rownames(true.cov.100.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")

true.cov.100.age.groups.table %>%
  kable() %>%
  kable_styling("striped") # Commented OCTOBER
Female.15.25 Female.25.40 Female.40.50
Male.15.25 7 0 0
Male.25.40 33 7 0
Male.40.50 17 15 0
write.csv(true.cov.100.age.groups.table, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_10_True_Pairings_at_100_Coverage.csv")

Male within 25 and 40 were much pairing with women between 15 and 25 years old, followed by men in 40-50 and women between 15-25, and men in 40 - 50 and women in 25 - 40 of age.

3.2 True pairings at different sampling (sequence) coverage scenarios

Within 35 and 40 simulation time, if we consider the true pairings of a proportion of those who were HIV positive in different sampling (sequencing) coverage, we have the following table:

# Cov 35

M.15.25.F.15.25.MAR.cov.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.true.M.40.50.F.40.50)


# Cov 40

M.15.25.F.15.25.MAR.cov.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.true.M.40.50.F.40.50)

# Cov 45


M.15.25.F.15.25.MAR.cov.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.true.M.40.50.F.40.50)


# Cov 50


M.15.25.F.15.25.MAR.cov.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.true.M.40.50.F.40.50)


# Cov 55


M.15.25.F.15.25.MAR.cov.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.true.M.40.50.F.40.50)


# Cov 60


M.15.25.F.15.25.MAR.cov.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.true.M.40.50.F.40.50)


# Cov 65


M.15.25.F.15.25.MAR.cov.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.true.M.40.50.F.40.50)

# Cov 70


M.15.25.F.15.25.MAR.cov.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.true.M.40.50.F.40.50)


# Cov 75


M.15.25.F.15.25.MAR.cov.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.true.M.40.50.F.40.50)


# Cov 80


M.15.25.F.15.25.MAR.cov.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.true.M.40.50.F.40.50)


# Cov 85


M.15.25.F.15.25.MAR.cov.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.true.M.40.50.F.40.50)

# Cov 90


M.15.25.F.15.25.MAR.cov.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.true.M.40.50.F.40.50)


# Cov 95


M.15.25.F.15.25.MAR.cov.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.true.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.true.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.true.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.true.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.true.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.true.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.true.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.true.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.true.M.40.50.F.40.50)



# Agregated table of pairings: true 


pairing_true_scenarios <- matrix(c(M.15.25.F.15.25.MAR.cov.35[2], M.15.25.F.15.25.MAR.cov.40[2], 
                                   M.15.25.F.15.25.MAR.cov.45[2], M.15.25.F.15.25.MAR.cov.50[2], 
                                   M.15.25.F.15.25.MAR.cov.55[2], M.15.25.F.15.25.MAR.cov.60[2], 
                                   M.15.25.F.15.25.MAR.cov.65[2], M.15.25.F.15.25.MAR.cov.70[2], 
                                   M.15.25.F.15.25.MAR.cov.75[2], M.15.25.F.15.25.MAR.cov.80[2], 
                                   M.15.25.F.15.25.MAR.cov.85[2], M.15.25.F.15.25.MAR.cov.90[2], 
                                   M.15.25.F.15.25.MAR.cov.95[2], M.15.25.F.15.25.cov.100[2],
                                   
                                   M.25.40.F.15.25.MAR.cov.35[2], M.25.40.F.15.25.MAR.cov.40[2], 
                                   M.25.40.F.15.25.MAR.cov.45[2], M.25.40.F.15.25.MAR.cov.50[2], 
                                   M.25.40.F.15.25.MAR.cov.55[2], M.25.40.F.15.25.MAR.cov.60[2], 
                                   M.25.40.F.15.25.MAR.cov.65[2], M.25.40.F.15.25.MAR.cov.70[2], 
                                   M.25.40.F.15.25.MAR.cov.75[2], M.25.40.F.15.25.MAR.cov.80[2], 
                                   M.25.40.F.15.25.MAR.cov.85[2], M.25.40.F.15.25.MAR.cov.90[2], 
                                   M.25.40.F.15.25.MAR.cov.95[2], M.25.40.F.15.25.cov.100[2],
                                   
                                   M.40.50.F.15.25.MAR.cov.35[2], M.40.50.F.15.25.MAR.cov.40[2], 
                                   M.40.50.F.15.25.MAR.cov.45[2], M.40.50.F.15.25.MAR.cov.50[2], 
                                   M.40.50.F.15.25.MAR.cov.55[2], M.40.50.F.15.25.MAR.cov.60[2], 
                                   M.40.50.F.15.25.MAR.cov.65[2], M.40.50.F.15.25.MAR.cov.70[2], 
                                   M.40.50.F.15.25.MAR.cov.75[2], M.40.50.F.15.25.MAR.cov.80[2], 
                                   M.40.50.F.15.25.MAR.cov.85[2], M.40.50.F.15.25.MAR.cov.90[2], 
                                   M.40.50.F.15.25.MAR.cov.95[2], M.40.50.F.15.25.cov.100[2],
                                   
                                   M.15.25.F.25.40.MAR.cov.35[2], M.15.25.F.25.40.MAR.cov.40[2], 
                                   M.15.25.F.25.40.MAR.cov.45[2], M.15.25.F.25.40.MAR.cov.50[2], 
                                   M.15.25.F.25.40.MAR.cov.55[2], M.15.25.F.25.40.MAR.cov.60[2], 
                                   M.15.25.F.25.40.MAR.cov.65[2], M.15.25.F.25.40.MAR.cov.70[2], 
                                   M.15.25.F.25.40.MAR.cov.75[2], M.15.25.F.25.40.MAR.cov.80[2], 
                                   M.15.25.F.25.40.MAR.cov.85[2], M.15.25.F.25.40.MAR.cov.90[2], 
                                   M.15.25.F.25.40.MAR.cov.95[2], M.15.25.F.25.40.cov.100[2],
                                   
                                   M.25.40.F.25.40.MAR.cov.35[2], M.25.40.F.25.40.MAR.cov.40[2], 
                                   M.25.40.F.25.40.MAR.cov.45[2], M.25.40.F.25.40.MAR.cov.50[2], 
                                   M.25.40.F.25.40.MAR.cov.55[2], M.25.40.F.25.40.MAR.cov.60[2], 
                                   M.25.40.F.25.40.MAR.cov.65[2], M.25.40.F.25.40.MAR.cov.70[2], 
                                   M.25.40.F.25.40.MAR.cov.75[2], M.25.40.F.25.40.MAR.cov.80[2], 
                                   M.25.40.F.25.40.MAR.cov.85[2], M.25.40.F.25.40.MAR.cov.90[2], 
                                   M.25.40.F.25.40.MAR.cov.95[2], M.25.40.F.25.40.cov.100[2],
                                   
                                   M.40.50.F.25.40.MAR.cov.35[2], M.40.50.F.25.40.MAR.cov.40[2], 
                                   M.40.50.F.25.40.MAR.cov.45[2], M.40.50.F.25.40.MAR.cov.50[2], 
                                   M.40.50.F.25.40.MAR.cov.55[2], M.40.50.F.25.40.MAR.cov.60[2], 
                                   M.40.50.F.25.40.MAR.cov.65[2], M.40.50.F.25.40.MAR.cov.70[2], 
                                   M.40.50.F.25.40.MAR.cov.75[2], M.40.50.F.25.40.MAR.cov.80[2], 
                                   M.40.50.F.25.40.MAR.cov.85[2], M.40.50.F.25.40.MAR.cov.90[2], 
                                   M.40.50.F.25.40.MAR.cov.95[2], M.40.50.F.25.40.cov.100[2],
                                   
                                   M.15.25.F.40.50.MAR.cov.35[2], M.15.25.F.40.50.MAR.cov.40[2], 
                                   M.15.25.F.40.50.MAR.cov.45[2], M.15.25.F.40.50.MAR.cov.50[2], 
                                   M.15.25.F.40.50.MAR.cov.55[2], M.15.25.F.40.50.MAR.cov.60[2], 
                                   M.15.25.F.40.50.MAR.cov.65[2], M.15.25.F.40.50.MAR.cov.70[2], 
                                   M.15.25.F.40.50.MAR.cov.75[2], M.15.25.F.40.50.MAR.cov.80[2], 
                                   M.15.25.F.40.50.MAR.cov.85[2], M.15.25.F.40.50.MAR.cov.90[2], 
                                   M.15.25.F.40.50.MAR.cov.95[2], M.15.25.F.40.50.cov.100[2],
                                   
                                   M.25.40.F.40.50.MAR.cov.35[2], M.25.40.F.40.50.MAR.cov.40[2], 
                                   M.25.40.F.40.50.MAR.cov.45[2], M.25.40.F.40.50.MAR.cov.50[2], 
                                   M.25.40.F.40.50.MAR.cov.55[2], M.25.40.F.40.50.MAR.cov.60[2], 
                                   M.25.40.F.40.50.MAR.cov.65[2], M.25.40.F.40.50.MAR.cov.70[2], 
                                   M.25.40.F.40.50.MAR.cov.75[2], M.25.40.F.40.50.MAR.cov.80[2], 
                                   M.25.40.F.40.50.MAR.cov.85[2], M.25.40.F.40.50.MAR.cov.90[2], 
                                   M.25.40.F.40.50.MAR.cov.95[2], M.25.40.F.40.50.cov.100[2],
                                   
                                   M.40.50.F.40.50.MAR.cov.35[2], M.40.50.F.40.50.MAR.cov.40[2], 
                                   M.40.50.F.40.50.MAR.cov.45[2], M.40.50.F.40.50.MAR.cov.50[2], 
                                   M.40.50.F.40.50.MAR.cov.55[2], M.40.50.F.40.50.MAR.cov.60[2], 
                                   M.40.50.F.40.50.MAR.cov.65[2], M.40.50.F.40.50.MAR.cov.70[2], 
                                   M.40.50.F.40.50.MAR.cov.75[2], M.40.50.F.40.50.MAR.cov.80[2], 
                                   M.40.50.F.40.50.MAR.cov.85[2], M.40.50.F.40.50.MAR.cov.90[2], 
                                   M.40.50.F.40.50.MAR.cov.95[2], M.40.50.F.40.50.cov.100[2]),
                                 
                                 ncol = 14,
                                 byrow = TRUE)

colnames(pairing_true_scenarios) <- c("35", "40", "45",
                                      "50", "55", "60",
                                      "65", "70", "75",
                                      "80", "85", "90",
                                      "95", "true_100")

rownames(pairing_true_scenarios) <- c("M.15.25.F.15.25", "M.25.40.F.15.25", "M.40.50.F.15.25",
                                      "M.15.25.F.25.40", "M.25.40.F.25.40", "M.40.50.F.25.40",
                                      "M.15.25.F.40.50", "M.25.40.F.40.50", "M.40.50.F.40.50")

pairing_true_scenarios %>%
  kable() %>%
  kable_styling("striped") # OK
35 40 45 50 55 60 65 70 75 80 85 90 95 true_100
M.15.25.F.15.25 1 2 2 3 3 3 3 4 4 4 4 5 5 7
M.25.40.F.15.25 3 4 4 5 6 7 8 10 11 12 13 15 16 33
M.40.50.F.15.25 1 1 1 2 2 2 3 3 4 4 5 5 6 17
M.15.25.F.25.40 0 0 0 0 0 0 0 0 0 0 0 0 0 0
M.25.40.F.25.40 0 1 1 1 1 1 2 2 2 3 3 3 3 7
M.40.50.F.25.40 1 1 1 2 2 3 3 3 4 4 5 5 5 15
M.15.25.F.40.50 0 0 0 0 0 0 0 0 0 0 0 0 0 0
M.25.40.F.40.50 0 0 0 0 0 0 0 0 0 0 0 0 0 0
M.40.50.F.40.50 0 0 0 0 0 0 0 0 0 0 0 0 0 0
write.csv(pairing_true_scenarios, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_11_True_Pairings_at_35_95_Coverage.csv")

Like in the previous table of 100% sampling coverage, male within 25 and 40 were much pairing with women between 15 and 25 years old, followed by men in 40-50 and women between 15-25, and men in 40 - 50 and women in 25 - 40 of age. In addition, we can see that as the sequence coverage increase the number of pairings becomes close to the true values at 100% coverage.

3.3 Pairings inferred from transmission clusters of a phylogenetic tree

Within 35 and 40 simulation time, if we consider pairings built from phylogenetic tree’s transmission clusters in different sampling (sequencing) coverage, we have the following table:

# Cov 35

M.15.25.F.15.25.MAR.cov.cl.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.35 <- quant.med(d.MAR.cov.35$cov.MAR.a.35.cl.M.40.50.F.40.50)



MAR.cov.35.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.35[2]), (M.15.25.F.25.40.MAR.cov.cl.35[2]), (M.15.25.F.40.50.MAR.cov.cl.35[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.35[2]), (M.25.40.F.25.40.MAR.cov.cl.35[2]), (M.25.40.F.40.50.MAR.cov.cl.35[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.35[2]), (M.40.50.F.25.40.MAR.cov.cl.35[2]), (M.40.50.F.40.50.MAR.cov.cl.35[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.35.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.35.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")
# 


# Cov 40

M.15.25.F.15.25.MAR.cov.cl.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.40 <- quant.med(d.MAR.cov.40$cov.MAR.a.40.cl.M.40.50.F.40.50)



MAR.cov.40.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.40[2]), (M.15.25.F.25.40.MAR.cov.cl.40[2]), (M.15.25.F.40.50.MAR.cov.cl.40[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.40[2]), (M.25.40.F.25.40.MAR.cov.cl.40[2]), (M.25.40.F.40.50.MAR.cov.cl.40[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.40[2]), (M.40.50.F.25.40.MAR.cov.cl.40[2]), (M.40.50.F.40.50.MAR.cov.cl.40[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.40.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.40.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")
# 


# Cov 45

M.15.25.F.15.25.MAR.cov.cl.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.45 <- quant.med(d.MAR.cov.45$cov.MAR.a.45.cl.M.40.50.F.40.50)



MAR.cov.45.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.45[2]), (M.15.25.F.25.40.MAR.cov.cl.45[2]), (M.15.25.F.40.50.MAR.cov.cl.45[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.45[2]), (M.25.40.F.25.40.MAR.cov.cl.45[2]), (M.25.40.F.40.50.MAR.cov.cl.45[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.45[2]), (M.40.50.F.25.40.MAR.cov.cl.45[2]), (M.40.50.F.40.50.MAR.cov.cl.45[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.45.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.45.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")

# Cov 50

M.15.25.F.15.25.MAR.cov.cl.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.50 <- quant.med(d.MAR.cov.50$cov.MAR.a.50.cl.M.40.50.F.40.50)



MAR.cov.50.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.50[2]), (M.15.25.F.25.40.MAR.cov.cl.50[2]), (M.15.25.F.40.50.MAR.cov.cl.50[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.50[2]), (M.25.40.F.25.40.MAR.cov.cl.50[2]), (M.25.40.F.40.50.MAR.cov.cl.50[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.50[2]), (M.40.50.F.25.40.MAR.cov.cl.50[2]), (M.40.50.F.40.50.MAR.cov.cl.50[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.50.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.50.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")


# Cov 55

M.15.25.F.15.25.MAR.cov.cl.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.55 <- quant.med(d.MAR.cov.55$cov.MAR.a.55.cl.M.40.50.F.40.50)



MAR.cov.55.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.55[2]), (M.15.25.F.25.40.MAR.cov.cl.55[2]), (M.15.25.F.40.50.MAR.cov.cl.55[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.55[2]), (M.25.40.F.25.40.MAR.cov.cl.55[2]), (M.25.40.F.40.50.MAR.cov.cl.55[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.55[2]), (M.40.50.F.25.40.MAR.cov.cl.55[2]), (M.40.50.F.40.50.MAR.cov.cl.55[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.55.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.55.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")

# Cov 60

M.15.25.F.15.25.MAR.cov.cl.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.60 <- quant.med(d.MAR.cov.60$cov.MAR.a.60.cl.M.40.50.F.40.50)



MAR.cov.60.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.60[2]), (M.15.25.F.25.40.MAR.cov.cl.60[2]), (M.15.25.F.40.50.MAR.cov.cl.60[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.60[2]), (M.25.40.F.25.40.MAR.cov.cl.60[2]), (M.25.40.F.40.50.MAR.cov.cl.60[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.60[2]), (M.40.50.F.25.40.MAR.cov.cl.60[2]), (M.40.50.F.40.50.MAR.cov.cl.60[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.60.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.60.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")


# Cov 65

M.15.25.F.15.25.MAR.cov.cl.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.65 <- quant.med(d.MAR.cov.65$cov.MAR.a.65.cl.M.40.50.F.40.50)



MAR.cov.65.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.65[2]), (M.15.25.F.25.40.MAR.cov.cl.65[2]), (M.15.25.F.40.50.MAR.cov.cl.65[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.65[2]), (M.25.40.F.25.40.MAR.cov.cl.65[2]), (M.25.40.F.40.50.MAR.cov.cl.65[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.65[2]), (M.40.50.F.25.40.MAR.cov.cl.65[2]), (M.40.50.F.40.50.MAR.cov.cl.65[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.65.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.65.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")

# Cov 70

M.15.25.F.15.25.MAR.cov.cl.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.70 <- quant.med(d.MAR.cov.70$cov.MAR.a.70.cl.M.40.50.F.40.50)



MAR.cov.70.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.70[2]), (M.15.25.F.25.40.MAR.cov.cl.70[2]), (M.15.25.F.40.50.MAR.cov.cl.70[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.70[2]), (M.25.40.F.25.40.MAR.cov.cl.70[2]), (M.25.40.F.40.50.MAR.cov.cl.70[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.70[2]), (M.40.50.F.25.40.MAR.cov.cl.70[2]), (M.40.50.F.40.50.MAR.cov.cl.70[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.70.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.70.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")

# Cov 75

M.15.25.F.15.25.MAR.cov.cl.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.75 <- quant.med(d.MAR.cov.75$cov.MAR.a.75.cl.M.40.50.F.40.50)



MAR.cov.75.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.75[2]), (M.15.25.F.25.40.MAR.cov.cl.75[2]), (M.15.25.F.40.50.MAR.cov.cl.75[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.75[2]), (M.25.40.F.25.40.MAR.cov.cl.75[2]), (M.25.40.F.40.50.MAR.cov.cl.75[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.75[2]), (M.40.50.F.25.40.MAR.cov.cl.75[2]), (M.40.50.F.40.50.MAR.cov.cl.75[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.75.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.75.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")


# Cov 80

M.15.25.F.15.25.MAR.cov.cl.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.80 <- quant.med(d.MAR.cov.80$cov.MAR.a.80.cl.M.40.50.F.40.50)



MAR.cov.80.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.80[2]), (M.15.25.F.25.40.MAR.cov.cl.80[2]), (M.15.25.F.40.50.MAR.cov.cl.80[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.80[2]), (M.25.40.F.25.40.MAR.cov.cl.80[2]), (M.25.40.F.40.50.MAR.cov.cl.80[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.80[2]), (M.40.50.F.25.40.MAR.cov.cl.80[2]), (M.40.50.F.40.50.MAR.cov.cl.80[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.80.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.80.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")


# Cov 85

M.15.25.F.15.25.MAR.cov.cl.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.85 <- quant.med(d.MAR.cov.85$cov.MAR.a.85.cl.M.40.50.F.40.50)



MAR.cov.85.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.85[2]), (M.15.25.F.25.40.MAR.cov.cl.85[2]), (M.15.25.F.40.50.MAR.cov.cl.85[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.85[2]), (M.25.40.F.25.40.MAR.cov.cl.85[2]), (M.25.40.F.40.50.MAR.cov.cl.85[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.85[2]), (M.40.50.F.25.40.MAR.cov.cl.85[2]), (M.40.50.F.40.50.MAR.cov.cl.85[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.85.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.85.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")

# Cov 90

M.15.25.F.15.25.MAR.cov.cl.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.90 <- quant.med(d.MAR.cov.90$cov.MAR.a.90.cl.M.40.50.F.40.50)



MAR.cov.90.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.90[2]), (M.15.25.F.25.40.MAR.cov.cl.90[2]), (M.15.25.F.40.50.MAR.cov.cl.90[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.90[2]), (M.25.40.F.25.40.MAR.cov.cl.90[2]), (M.25.40.F.40.50.MAR.cov.cl.90[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.90[2]), (M.40.50.F.25.40.MAR.cov.cl.90[2]), (M.40.50.F.40.50.MAR.cov.cl.90[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.90.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.90.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")

# Cov 95

M.15.25.F.15.25.MAR.cov.cl.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.M.15.25.F.15.25)
M.25.40.F.15.25.MAR.cov.cl.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.M.25.40.F.15.25)
M.40.50.F.15.25.MAR.cov.cl.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.M.40.50.F.15.25)

M.15.25.F.25.40.MAR.cov.cl.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.M.15.25.F.25.40)
M.25.40.F.25.40.MAR.cov.cl.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.M.25.40.F.25.40)
M.40.50.F.25.40.MAR.cov.cl.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.M.40.50.F.25.40)

M.15.25.F.40.50.MAR.cov.cl.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.M.15.25.F.40.50)
M.25.40.F.40.50.MAR.cov.cl.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.M.25.40.F.40.50)
M.40.50.F.40.50.MAR.cov.cl.95 <- quant.med(d.MAR.cov.95$cov.MAR.a.95.cl.M.40.50.F.40.50)



MAR.cov.95.cl.age.groups.table <- matrix(c((M.15.25.F.15.25.MAR.cov.cl.95[2]), (M.15.25.F.25.40.MAR.cov.cl.95[2]), (M.15.25.F.40.50.MAR.cov.cl.95[2]),
                                           (M.25.40.F.15.25.MAR.cov.cl.95[2]), (M.25.40.F.25.40.MAR.cov.cl.95[2]), (M.25.40.F.40.50.MAR.cov.cl.95[2]),
                                           (M.40.50.F.15.25.MAR.cov.cl.95[2]), (M.40.50.F.25.40.MAR.cov.cl.95[2]), (M.40.50.F.40.50.MAR.cov.cl.95[2])),
                                         ncol = 3,
                                         byrow = TRUE)

# colnames(MAR.cov.95.cl.age.groups.table) <- c("Female.15.25", "Female.25.40", "Female.40.50")
# rownames(MAR.cov.95.cl.age.groups.table) <- c("Male.15.25", "Male.25.40", "Male.40.50")
# 


# Agregated table of pairings in transmission clusters  

pairing_clust_inf_scenarios <- matrix(c(M.15.25.F.15.25.MAR.cov.cl.35[2], M.15.25.F.15.25.MAR.cov.cl.40[2], 
                                        M.15.25.F.15.25.MAR.cov.cl.45[2], M.15.25.F.15.25.MAR.cov.cl.50[2], 
                                        M.15.25.F.15.25.MAR.cov.cl.55[2], M.15.25.F.15.25.MAR.cov.cl.60[2], 
                                        M.15.25.F.15.25.MAR.cov.cl.65[2], M.15.25.F.15.25.MAR.cov.cl.70[2], 
                                        M.15.25.F.15.25.MAR.cov.cl.75[2], M.15.25.F.15.25.MAR.cov.cl.80[2], 
                                        M.15.25.F.15.25.MAR.cov.cl.85[2], M.15.25.F.15.25.MAR.cov.cl.90[2], 
                                        M.15.25.F.15.25.MAR.cov.cl.95[2], M.15.25.F.15.25.cov.100[2],
                                        
                                        M.25.40.F.15.25.MAR.cov.cl.35[2], M.25.40.F.15.25.MAR.cov.cl.40[2], 
                                        M.25.40.F.15.25.MAR.cov.cl.45[2], M.25.40.F.15.25.MAR.cov.cl.50[2], 
                                        M.25.40.F.15.25.MAR.cov.cl.55[2], M.25.40.F.15.25.MAR.cov.cl.60[2], 
                                        M.25.40.F.15.25.MAR.cov.cl.65[2], M.25.40.F.15.25.MAR.cov.cl.70[2], 
                                        M.25.40.F.15.25.MAR.cov.cl.75[2], M.25.40.F.15.25.MAR.cov.cl.80[2], 
                                        M.25.40.F.15.25.MAR.cov.cl.85[2], M.25.40.F.15.25.MAR.cov.cl.90[2], 
                                        M.25.40.F.15.25.MAR.cov.cl.95[2], M.25.40.F.15.25.cov.100[2],
                                        
                                        M.40.50.F.15.25.MAR.cov.cl.35[2], M.40.50.F.15.25.MAR.cov.cl.40[2], 
                                        M.40.50.F.15.25.MAR.cov.cl.45[2], M.40.50.F.15.25.MAR.cov.cl.50[2], 
                                        M.40.50.F.15.25.MAR.cov.cl.55[2], M.40.50.F.15.25.MAR.cov.cl.60[2], 
                                        M.40.50.F.15.25.MAR.cov.cl.65[2], M.40.50.F.15.25.MAR.cov.cl.70[2], 
                                        M.40.50.F.15.25.MAR.cov.cl.75[2], M.40.50.F.15.25.MAR.cov.cl.80[2], 
                                        M.40.50.F.15.25.MAR.cov.cl.85[2], M.40.50.F.15.25.MAR.cov.cl.90[2], 
                                        M.40.50.F.15.25.MAR.cov.cl.95[2], M.40.50.F.15.25.cov.100[2],
                                        
                                        M.15.25.F.25.40.MAR.cov.cl.35[2], M.15.25.F.25.40.MAR.cov.cl.40[2], 
                                        M.15.25.F.25.40.MAR.cov.cl.45[2], M.15.25.F.25.40.MAR.cov.cl.50[2], 
                                        M.15.25.F.25.40.MAR.cov.cl.55[2], M.15.25.F.25.40.MAR.cov.cl.60[2], 
                                        M.15.25.F.25.40.MAR.cov.cl.65[2], M.15.25.F.25.40.MAR.cov.cl.70[2], 
                                        M.15.25.F.25.40.MAR.cov.cl.75[2], M.15.25.F.25.40.MAR.cov.cl.80[2], 
                                        M.15.25.F.25.40.MAR.cov.cl.85[2], M.15.25.F.25.40.MAR.cov.cl.90[2], 
                                        M.15.25.F.25.40.MAR.cov.cl.95[2], M.15.25.F.25.40.cov.100[2],
                                        
                                        M.25.40.F.25.40.MAR.cov.cl.35[2], M.25.40.F.25.40.MAR.cov.cl.40[2], 
                                        M.25.40.F.25.40.MAR.cov.cl.45[2], M.25.40.F.25.40.MAR.cov.cl.50[2], 
                                        M.25.40.F.25.40.MAR.cov.cl.55[2], M.25.40.F.25.40.MAR.cov.cl.60[2], 
                                        M.25.40.F.25.40.MAR.cov.cl.65[2], M.25.40.F.25.40.MAR.cov.cl.70[2], 
                                        M.25.40.F.25.40.MAR.cov.cl.75[2], M.25.40.F.25.40.MAR.cov.cl.80[2], 
                                        M.25.40.F.25.40.MAR.cov.cl.85[2], M.25.40.F.25.40.MAR.cov.cl.90[2], 
                                        M.25.40.F.25.40.MAR.cov.cl.95[2], M.25.40.F.25.40.cov.100[2],
                                        
                                        M.40.50.F.25.40.MAR.cov.cl.35[2], M.40.50.F.25.40.MAR.cov.cl.40[2], 
                                        M.40.50.F.25.40.MAR.cov.cl.45[2], M.40.50.F.25.40.MAR.cov.cl.50[2], 
                                        M.40.50.F.25.40.MAR.cov.cl.55[2], M.40.50.F.25.40.MAR.cov.cl.60[2], 
                                        M.40.50.F.25.40.MAR.cov.cl.65[2], M.40.50.F.25.40.MAR.cov.cl.70[2], 
                                        M.40.50.F.25.40.MAR.cov.cl.75[2], M.40.50.F.25.40.MAR.cov.cl.80[2], 
                                        M.40.50.F.25.40.MAR.cov.cl.85[2], M.40.50.F.25.40.MAR.cov.cl.90[2], 
                                        M.40.50.F.25.40.MAR.cov.cl.95[2], M.40.50.F.25.40.cov.100[2],
                                        
                                        M.15.25.F.40.50.MAR.cov.cl.35[2], M.15.25.F.40.50.MAR.cov.cl.40[2], 
                                        M.15.25.F.40.50.MAR.cov.cl.45[2], M.15.25.F.40.50.MAR.cov.cl.50[2], 
                                        M.15.25.F.40.50.MAR.cov.cl.55[2], M.15.25.F.40.50.MAR.cov.cl.60[2], 
                                        M.15.25.F.40.50.MAR.cov.cl.65[2], M.15.25.F.40.50.MAR.cov.cl.70[2], 
                                        M.15.25.F.40.50.MAR.cov.cl.75[2], M.15.25.F.40.50.MAR.cov.cl.80[2], 
                                        M.15.25.F.40.50.MAR.cov.cl.85[2], M.15.25.F.40.50.MAR.cov.cl.90[2], 
                                        M.15.25.F.40.50.MAR.cov.cl.95[2], M.15.25.F.40.50.cov.100[2],
                                        
                                        M.25.40.F.40.50.MAR.cov.cl.35[2], M.25.40.F.40.50.MAR.cov.cl.40[2], 
                                        M.25.40.F.40.50.MAR.cov.cl.45[2], M.25.40.F.40.50.MAR.cov.cl.50[2], 
                                        M.25.40.F.40.50.MAR.cov.cl.55[2], M.25.40.F.40.50.MAR.cov.cl.60[2], 
                                        M.25.40.F.40.50.MAR.cov.cl.65[2], M.25.40.F.40.50.MAR.cov.cl.70[2], 
                                        M.25.40.F.40.50.MAR.cov.cl.75[2], M.25.40.F.40.50.MAR.cov.cl.80[2], 
                                        M.25.40.F.40.50.MAR.cov.cl.85[2], M.25.40.F.40.50.MAR.cov.cl.90[2], 
                                        M.25.40.F.40.50.MAR.cov.cl.95[2], M.25.40.F.40.50.cov.100[2],
                                        
                                        M.40.50.F.40.50.MAR.cov.cl.35[2], M.40.50.F.40.50.MAR.cov.cl.40[2], 
                                        M.40.50.F.40.50.MAR.cov.cl.45[2], M.40.50.F.40.50.MAR.cov.cl.50[2], 
                                        M.40.50.F.40.50.MAR.cov.cl.55[2], M.40.50.F.40.50.MAR.cov.cl.60[2], 
                                        M.40.50.F.40.50.MAR.cov.cl.65[2], M.40.50.F.40.50.MAR.cov.cl.70[2], 
                                        M.40.50.F.40.50.MAR.cov.cl.75[2], M.40.50.F.40.50.MAR.cov.cl.80[2], 
                                        M.40.50.F.40.50.MAR.cov.cl.85[2], M.40.50.F.40.50.MAR.cov.cl.90[2], 
                                        M.40.50.F.40.50.MAR.cov.cl.95[2], M.40.50.F.40.50.cov.100[2]),
                                      
                                      ncol = 14,
                                      byrow = TRUE)



colnames(pairing_clust_inf_scenarios) <- c("35", "40", "45",
                                           "50", "55", "60",
                                           "65", "70", "75",
                                           "80", "85", "90",
                                           "95", "true_100")

rownames(pairing_clust_inf_scenarios) <- c("M.15.25.F.15.25", "M.25.40.F.15.25", "M.40.50.F.15.25",
                                           "M.15.25.F.25.40", "M.25.40.F.25.40", "M.40.50.F.25.40",
                                           "M.15.25.F.40.50", "M.25.40.F.40.50", "M.40.50.F.40.50")
# 
# 
pairing_clust_inf_scenarios %>%
  kable() %>%
  kable_styling("striped") # OK
35 40 45 50 55 60 65 70 75 80 85 90 95 true_100
M.15.25.F.15.25 1 2 2 3 3.5 4 4 5 6 6 6 7 9 7
M.25.40.F.15.25 2 2 3 4 5.0 6 6 8 10 12 13 16 18 33
M.40.50.F.15.25 0 0 1 1 1.0 2 2 2 3 4 4 4 6 17
M.15.25.F.25.40 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0
M.25.40.F.25.40 0 0 0 0 1.0 1 1 1 1 1 2 2 2 7
M.40.50.F.25.40 0 0 1 1 1.0 1 2 2 2 2 3 3 3 15
M.15.25.F.40.50 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0
M.25.40.F.40.50 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0
M.40.50.F.40.50 0 0 0 0 0.0 0 0 0 0 0 0 0 0 0
write.csv(pairing_clust_inf_scenarios, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_12_Inf_Pairings_at_35_95_Coverage.csv")

4 Proportions of pairings across age groups

With transmission clusters and pairings we can be able to compute the proportions of men/women from a given age group who are in within any transmission cluster. This means, for example if we have a transmission cluster with M men and W women for example, knowing also their age, we can place these men and women in the age groups (< 25 year, 25 - 40 years, and 40 - 50 years), and thereafter compute their proportions: number of men in a given age group who are in pair with women in another given age group.

4.1 True proportions at 100% sampling (sequence) coverage

Within 35 - 40 simulation, hen we consider 100% sampling (sequence) coverage, the true proprotions of men/women are given in the following table:

dr.cov.100 <- dr %>%
  select(contains(".100"))

d.MAR.cov.100.prop.men <-  dr.cov.100 %>%
  select(contains("true.prop.men")) # true proportion of pairings inferred 

d.MAR.cov.100.prop.women <-  dr.cov.100 %>%
  select(contains("true.prop.women")) 



vector.MAR.true.cov.100.prop.men15.25.F.15.25 <- d.MAR.cov.100.prop.men[,1]
vector.MAR.true.cov.100.prop.women15.25.M.15.25 <- d.MAR.cov.100.prop.women[,1]

vector.MAR.true.cov.100.prop.men25.40.F.15.25 <- d.MAR.cov.100.prop.men[,2]
vector.MAR.true.cov.100.prop.women25.40.M.15.25 <- d.MAR.cov.100.prop.women[,2]

vector.MAR.true.cov.100.prop.men40.50.F.15.25 <- d.MAR.cov.100.prop.men[,3]
vector.MAR.true.cov.100.prop.women40.50.M.15.25 <- d.MAR.cov.100.prop.women[,3]

vector.MAR.true.cov.100.prop.men15.25.F.25.40 <- d.MAR.cov.100.prop.men[,4]
vector.MAR.true.cov.100.prop.women15.25.M.25.40 <- d.MAR.cov.100.prop.women[,4]

vector.MAR.true.cov.100.prop.men25.40.F.25.40 <- d.MAR.cov.100.prop.men[,5]
vector.MAR.true.cov.100.prop.women25.40.M.25.40 <- d.MAR.cov.100.prop.women[,5]

vector.MAR.true.cov.100.prop.men40.50.F.25.40 <- d.MAR.cov.100.prop.men[,6]
vector.MAR.true.cov.100.prop.women40.50.M.25.40 <- d.MAR.cov.100.prop.women[,6]


vector.MAR.true.cov.100.prop.men15.25.F.40.50 <- d.MAR.cov.100.prop.men[,7]
vector.MAR.true.cov.100.prop.women15.25.M.40.50 <- d.MAR.cov.100.prop.women[,7]

vector.MAR.true.cov.100.prop.men25.40.F.40.50 <- d.MAR.cov.100.prop.men[,8]
vector.MAR.true.cov.100.prop.women25.40.M.40.50 <- d.MAR.cov.100.prop.women[,8]

vector.MAR.true.cov.100.prop.men40.50.F.40.50 <- d.MAR.cov.100.prop.men[,9]
vector.MAR.true.cov.100.prop.women40.50.M.40.50 <- d.MAR.cov.100.prop.women[,9]


# Summarrised

d.MAR.true.cov.100.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.100.prop.men[,1])
d.MAR.true.cov.100.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.100.prop.women[,1])

d.MAR.true.cov.100.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.100.prop.men[,2])
d.MAR.true.cov.100.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.100.prop.women[,2])

d.MAR.true.cov.100.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.100.prop.men[,3])
d.MAR.true.cov.100.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.100.prop.women[,3])

d.MAR.true.cov.100.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.100.prop.men[,4])
d.MAR.true.cov.100.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.100.prop.women[,4])

d.MAR.true.cov.100.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.100.prop.men[,5])
d.MAR.true.cov.100.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.100.prop.women[,5])

d.MAR.true.cov.100.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.100.prop.men[,6])
d.MAR.true.cov.100.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.100.prop.women[,6])


d.MAR.true.cov.100.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.100.prop.men[,7])
d.MAR.true.cov.100.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.100.prop.women[,7])

d.MAR.true.cov.100.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.100.prop.men[,8])
d.MAR.true.cov.100.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.100.prop.women[,8])

d.MAR.true.cov.100.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.100.prop.men[,9])
d.MAR.true.cov.100.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.100.prop.women[,9])

props.val.F <- c(d.MAR.true.cov.100.prop.men15.25.F.15.25[2], d.MAR.true.cov.100.prop.women15.25.M.15.25[2],
                 d.MAR.true.cov.100.prop.men25.40.F.15.25[2], d.MAR.true.cov.100.prop.women25.40.M.15.25[2],
                 d.MAR.true.cov.100.prop.men40.50.F.15.25[2], d.MAR.true.cov.100.prop.women40.50.M.15.25[2],
                 d.MAR.true.cov.100.prop.men15.25.F.25.40[2], d.MAR.true.cov.100.prop.women15.25.M.25.40[2],
                 d.MAR.true.cov.100.prop.men25.40.F.25.40[2], d.MAR.true.cov.100.prop.women25.40.M.25.40[2],
                 d.MAR.true.cov.100.prop.men40.50.F.25.40[2], d.MAR.true.cov.100.prop.women40.50.M.25.40[2],
                 d.MAR.true.cov.100.prop.men15.25.F.40.50[2], d.MAR.true.cov.100.prop.women15.25.M.40.50[2],
                 d.MAR.true.cov.100.prop.men25.40.F.40.50[2], d.MAR.true.cov.100.prop.women25.40.M.40.50[2],
                 d.MAR.true.cov.100.prop.men40.50.F.40.50[2], d.MAR.true.cov.100.prop.women40.50.M.40.50[2])

props.val.U <- c(d.MAR.true.cov.100.prop.men15.25.F.15.25[3], d.MAR.true.cov.100.prop.women15.25.M.15.25[3],
                 d.MAR.true.cov.100.prop.men25.40.F.15.25[3], d.MAR.true.cov.100.prop.women25.40.M.15.25[3],
                 d.MAR.true.cov.100.prop.men40.50.F.15.25[3], d.MAR.true.cov.100.prop.women40.50.M.15.25[3],
                 d.MAR.true.cov.100.prop.men15.25.F.25.40[3], d.MAR.true.cov.100.prop.women15.25.M.25.40[3],
                 d.MAR.true.cov.100.prop.men25.40.F.25.40[3], d.MAR.true.cov.100.prop.women25.40.M.25.40[3],
                 d.MAR.true.cov.100.prop.men40.50.F.25.40[3], d.MAR.true.cov.100.prop.women40.50.M.25.40[3],
                 d.MAR.true.cov.100.prop.men15.25.F.40.50[3], d.MAR.true.cov.100.prop.women15.25.M.40.50[3],
                 d.MAR.true.cov.100.prop.men25.40.F.40.50[3], d.MAR.true.cov.100.prop.women25.40.M.40.50[3],
                 d.MAR.true.cov.100.prop.men40.50.F.40.50[3], d.MAR.true.cov.100.prop.women40.50.M.40.50[3])



props.val.L <- c(d.MAR.true.cov.100.prop.men15.25.F.15.25[1], d.MAR.true.cov.100.prop.women15.25.M.15.25[1],
                 d.MAR.true.cov.100.prop.men25.40.F.15.25[1], d.MAR.true.cov.100.prop.women25.40.M.15.25[1],
                 d.MAR.true.cov.100.prop.men40.50.F.15.25[1], d.MAR.true.cov.100.prop.women40.50.M.15.25[1],
                 d.MAR.true.cov.100.prop.men15.25.F.25.40[1], d.MAR.true.cov.100.prop.women15.25.M.25.40[1],
                 d.MAR.true.cov.100.prop.men25.40.F.25.40[1], d.MAR.true.cov.100.prop.women25.40.M.25.40[1],
                 d.MAR.true.cov.100.prop.men40.50.F.25.40[1], d.MAR.true.cov.100.prop.women40.50.M.25.40[1],
                 d.MAR.true.cov.100.prop.men15.25.F.40.50[1], d.MAR.true.cov.100.prop.women15.25.M.40.50[1],
                 d.MAR.true.cov.100.prop.men25.40.F.40.50[1], d.MAR.true.cov.100.prop.women25.40.M.40.50[1],
                 d.MAR.true.cov.100.prop.men40.50.F.40.50[1], d.MAR.true.cov.100.prop.women40.50.M.40.50[1])


names.props <- c("M.15.25.F.15.25", "F.15.25.M.15.25", "M.25.40.F.15.25", "F.25.40.M.15.25",
                 "M.40.50.F.15.25", "F.40.50.M.15.25", "M.15.25.F.25.40", "F.15.25.M.25.40",
                 "M.25.40.F.25.40", "F.25.40.M.25.40", "M.40.50.F.25.40", "F.40.50.M.25.40",
                 "M.15.25.F.40.50", "F.15.25.M.40.50", "M.25.40.F.40.50", "F.25.40.M.40.50",
                 "M.40.50.F.40.50", "F.40.50.M.40.50")


prop_pairings_100 <- data.frame(names.props, props.val.L, props.val.F, props.val.U)

colnames(prop_pairings_100) <- c("name", "lower.Q1", "med", "upper.Q3")

# prop_pairings_100 %>%
#   kable() %>%
#   kable_styling("striped") # Commented in OCTOBER



# For problematic age groups

prop_pairings_100_target <- prop_pairings_100[-c(4, 6, 7, 12, 13, 15, 17, 18),]

prop_pairings_100_target %>%
  kable() %>%
  kable_styling("striped")
name lower.Q1 med upper.Q3
1 M.15.25.F.15.25 0.8888889 1.0000000 1.0000000
2 F.15.25.M.15.25 0.0810811 0.1390374 0.2038927
3 M.25.40.F.15.25 0.7619048 0.8260870 0.8780488
5 M.40.50.F.15.25 0.4152299 0.5185185 0.6153846
8 F.15.25.M.25.40 0.4708014 0.5562914 0.6361643
9 M.25.40.F.25.40 0.1219512 0.1739130 0.2380952
10 F.25.40.M.25.40 0.1818182 0.2800000 0.4000000
11 M.40.50.F.25.40 0.3823529 0.4782609 0.5773525
14 F.15.25.M.40.50 0.1945377 0.2779292 0.3780520
16 F.25.40.M.40.50 0.5555556 0.6956522 0.8000000
#


write.csv(prop_pairings_100_target, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_13_True_Proportion_of_Pairings_at_100_Coverage.csv")




d <- as.data.frame(prop_pairings_100_target)
rownames(d) <- d$name
d <- d[order(row.names(d)), ]
d_f <- d

d_f$age_groups <- c(rep(c("15_24 & 15_24", "15_24 & 25_39", "15_24 & 40_49", "25_39 & 25_39", "25_39 & 40_49"), 2))

x <- c(rep(c("15_24 & 15_24", "15_24 & 25_39", "15_24 & 40_49", "25_39 & 25_39", "25_39 & 40_49"), 2))

d_f$x <- x
d_f$f_m <- c(rep("Females_Males", 5), rep("Males_Females", 5))
plots.prop_pairings_100_target  <- ggplot(d_f, aes(x=x, y=med, colour=age_groups)) + 
  geom_errorbar(aes(ymin=lower.Q1, ymax=upper.Q3), width=.5) +
  geom_line(size=.5) +
  geom_point(size=1) + 
  facet_grid(.~f_m)+
  theme(legend.position="top")+
  xlab("Age Groups for proportions of pairings") + ylab("Proportion") 

# OR

# plots.prop_pairings_100_target  <- ggplot(d_f, aes(x=x, y=med, colour=f_m)) + 
#   geom_errorbar(aes(ymin=lower.Q1, ymax=upper.Q3), width=.5) +
#   geom_line(size=.5) +
#   geom_point(size=1) + 
#   # facet_grid(.~f_m)+
#   theme(legend.position="top")+
#   xlab("Age Groups for proportions of pairings") + ylab("Proportion") 


print(plots.prop_pairings_100_target)
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

ggsave(filename = "Plot_a_8_True_Proportion_of_Pairings_at_100_Coverage.pdf",
       plot = plots.prop_pairings_100_target,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 26, height = 15, units = "cm")
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

4.2 Proportions in different sequence coverage scenarios: for pairings inferred from transmission clusters

Proportions of men/women inferred from pairings in transmission clusters.

# Extracting data

# Proportions of pairing of men/women across different age groups

# (i) transmission network built from transmission clusters
# .cl.prop.men
# .cl.prop.women

# (ii) true proportions in true transmission network of these individuals in transmission clusters
# .cl.true.prop.men
# .cl.true.prop.women


# MAR





d.MAR <- dr %>%
  select(contains("MAR."))


d.MAR.cov.35 <- d.MAR %>%
  select(contains("cov.MAR.a.35.")) 
d.MAR.cov.40 <- d.MAR %>%
  select(contains("cov.MAR.a.40.")) 
d.MAR.cov.45 <- d.MAR %>%
  select(contains("cov.MAR.a.45.")) 
d.MAR.cov.50 <- d.MAR %>%
  select(contains("cov.MAR.a.50.")) 
d.MAR.cov.55 <- d.MAR %>%
  select(contains("cov.MAR.a.55.")) 
d.MAR.cov.60 <- d.MAR %>%
  select(contains("cov.MAR.a.60.")) 
d.MAR.cov.65 <- d.MAR %>%
  select(contains("cov.MAR.a.65.")) 
d.MAR.cov.70 <- d.MAR %>%
  select(contains("cov.MAR.a.70.")) 
d.MAR.cov.75 <- d.MAR %>%
  select(contains("cov.MAR.a.75.")) 
d.MAR.cov.80 <- d.MAR %>%
  select(contains("cov.MAR.a.80.")) 
d.MAR.cov.85 <- d.MAR %>%
  select(contains("cov.MAR.a.85.")) 
d.MAR.cov.90 <- d.MAR %>%
  select(contains("cov.MAR.a.90.")) 
d.MAR.cov.95 <- d.MAR %>%
  select(contains("cov.MAR.a.95.")) 
d.MAR.cov.100 <- dr %>%
  select(contains("cov.MCAR.100")) 



# cov 35

d.MAR.cov.35.cl.prop.men <-  d.MAR.cov.35 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.35.cl.prop.women <-  d.MAR.cov.35 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.35.cl.true.prop.men <-  d.MAR.cov.35 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.35.cl.true.prop.women <-  d.MAR.cov.35 %>%
  select(contains(".cl.true.prop.women")) 



# cov 40

d.MAR.cov.40.cl.prop.men <-  d.MAR.cov.40 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.40.cl.prop.women <-  d.MAR.cov.40 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.40.cl.true.prop.men <-  d.MAR.cov.40 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.40.cl.true.prop.women <-  d.MAR.cov.40 %>%
  select(contains(".cl.true.prop.women")) 


# cov 45

d.MAR.cov.45.cl.prop.men <-  d.MAR.cov.45 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.45.cl.prop.women <-  d.MAR.cov.45 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.45.cl.true.prop.men <-  d.MAR.cov.45 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.45.cl.true.prop.women <-  d.MAR.cov.45 %>%
  select(contains(".cl.true.prop.women")) 



# cov 50

d.MAR.cov.50.cl.prop.men <-  d.MAR.cov.50 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.50.cl.prop.women <-  d.MAR.cov.50 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.50.cl.true.prop.men <-  d.MAR.cov.50 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.50.cl.true.prop.women <-  d.MAR.cov.50 %>%
  select(contains(".cl.true.prop.women")) 



# cov 55

d.MAR.cov.55.cl.prop.men <-  d.MAR.cov.55 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.55.cl.prop.women <-  d.MAR.cov.55 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.55.cl.true.prop.men <-  d.MAR.cov.55 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.55.cl.true.prop.women <-  d.MAR.cov.55 %>%
  select(contains(".cl.true.prop.women")) 



# cov 60

d.MAR.cov.60.cl.prop.men <-  d.MAR.cov.60 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.60.cl.prop.women <-  d.MAR.cov.60 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.60.cl.true.prop.men <-  d.MAR.cov.60 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.60.cl.true.prop.women <-  d.MAR.cov.60 %>%
  select(contains(".cl.true.prop.women")) 



# 65

d.MAR.cov.65.cl.prop.men <-  d.MAR.cov.65 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.65.cl.prop.women <-  d.MAR.cov.65 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.65.cl.true.prop.men <-  d.MAR.cov.65 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.65.cl.true.prop.women <-  d.MAR.cov.65 %>%
  select(contains(".cl.true.prop.women")) 



# 70

d.MAR.cov.70.cl.prop.men <-  d.MAR.cov.70 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.70.cl.prop.women <-  d.MAR.cov.70 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.70.cl.true.prop.men <-  d.MAR.cov.70 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.70.cl.true.prop.women <-  d.MAR.cov.70 %>%
  select(contains(".cl.true.prop.women")) 



# 75

d.MAR.cov.75.cl.prop.men <-  d.MAR.cov.75 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.75.cl.prop.women <-  d.MAR.cov.75 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.75.cl.true.prop.men <-  d.MAR.cov.75 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.75.cl.true.prop.women <-  d.MAR.cov.75 %>%
  select(contains(".cl.true.prop.women")) 



# cov 80

d.MAR.cov.80.cl.prop.men <-  d.MAR.cov.80 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.80.cl.prop.women <-  d.MAR.cov.80 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.80.cl.true.prop.men <-  d.MAR.cov.80 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.80.cl.true.prop.women <-  d.MAR.cov.80 %>%
  select(contains(".cl.true.prop.women")) 



# cov 85

d.MAR.cov.85.cl.prop.men <-  d.MAR.cov.85 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.85.cl.prop.women <-  d.MAR.cov.85 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.85.cl.true.prop.men <-  d.MAR.cov.85 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.85.cl.true.prop.women <-  d.MAR.cov.85 %>%
  select(contains(".cl.true.prop.women")) 



# cov 90

d.MAR.cov.90.cl.prop.men <-  d.MAR.cov.90 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.90.cl.prop.women <-  d.MAR.cov.90 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.90.cl.true.prop.men <-  d.MAR.cov.90 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.90.cl.true.prop.women <-  d.MAR.cov.90 %>%
  select(contains(".cl.true.prop.women")) 



# cov 95


d.MAR.cov.95.cl.prop.men <-  d.MAR.cov.95 %>%
  select(contains(".cl.prop.men")) # proportion of pairings inferred from transmission clusters

d.MAR.cov.95.cl.prop.women <-  d.MAR.cov.95 %>%
  select(contains(".cl.prop.women")) 

d.MAR.cov.95.cl.true.prop.men <-  d.MAR.cov.95 %>%
  select(contains(".cl.true.prop.men"))  # true proportion of pairings in transmission clusters

d.MAR.cov.95.cl.true.prop.women <-  d.MAR.cov.95 %>%
  select(contains(".cl.true.prop.women")) 




# proportions computation


# Cov 35


# Vectors

vector.MAR.cov.35.cl.prop.men15.25.F.15.25 <- d.MAR.cov.35.cl.prop.men[,1]
vector.MAR.cov.35.cl.prop.women15.25.M.15.25 <- d.MAR.cov.35.cl.prop.women[,1]

vector.MAR.cov.35.cl.prop.men25.40.F.15.25 <- d.MAR.cov.35.cl.prop.men[,2]
vector.MAR.cov.35.cl.prop.women25.40.M.15.25 <- d.MAR.cov.35.cl.prop.women[,2]

vector.MAR.cov.35.cl.prop.men40.50.F.15.25 <- d.MAR.cov.35.cl.prop.men[,3]
vector.MAR.cov.35.cl.prop.women40.50.M.15.25 <- d.MAR.cov.35.cl.prop.women[,3]

vector.MAR.cov.35.cl.prop.men15.25.F.25.40 <- d.MAR.cov.35.cl.prop.men[,4]
vector.MAR.cov.35.cl.prop.women15.25.M.25.40 <- d.MAR.cov.35.cl.prop.women[,4]

vector.MAR.cov.35.cl.prop.men25.40.F.25.40 <- d.MAR.cov.35.cl.prop.men[,5]
vector.MAR.cov.35.cl.prop.women25.40.M.25.40 <- d.MAR.cov.35.cl.prop.women[,5]

vector.MAR.cov.35.cl.prop.men40.50.F.25.40 <- d.MAR.cov.35.cl.prop.men[,6]
vector.MAR.cov.35.cl.prop.women40.50.M.25.40 <- d.MAR.cov.35.cl.prop.women[,6]


vector.MAR.cov.35.cl.prop.men15.25.F.40.50 <- d.MAR.cov.35.cl.prop.men[,7]
vector.MAR.cov.35.cl.prop.women15.25.M.40.50 <- d.MAR.cov.35.cl.prop.women[,7]

vector.MAR.cov.35.cl.prop.men25.40.F.40.50 <- d.MAR.cov.35.cl.prop.men[,8]
vector.MAR.cov.35.cl.prop.women25.40.M.40.50 <- d.MAR.cov.35.cl.prop.women[,8]

vector.MAR.cov.35.cl.prop.men40.50.F.40.50 <- d.MAR.cov.35.cl.prop.men[,9]
vector.MAR.cov.35.cl.prop.women40.50.M.40.50 <- d.MAR.cov.35.cl.prop.women[,9]



# Summarised

d.MAR.cov.35.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.35.cl.prop.men[,1])
d.MAR.cov.35.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.35.cl.prop.women[,1])

d.MAR.cov.35.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.35.cl.prop.men[,2])
d.MAR.cov.35.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.35.cl.prop.women[,2])

d.MAR.cov.35.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.35.cl.prop.men[,3])
d.MAR.cov.35.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.35.cl.prop.women[,3])

d.MAR.cov.35.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.35.cl.prop.men[,4])
d.MAR.cov.35.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.35.cl.prop.women[,4])

d.MAR.cov.35.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.35.cl.prop.men[,5])
d.MAR.cov.35.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.35.cl.prop.women[,5])

d.MAR.cov.35.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.35.cl.prop.men[,6])
d.MAR.cov.35.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.35.cl.prop.women[,6])


d.MAR.cov.35.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.35.cl.prop.men[,7])
d.MAR.cov.35.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.35.cl.prop.women[,7])

d.MAR.cov.35.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.35.cl.prop.men[,8])
d.MAR.cov.35.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.35.cl.prop.women[,8])

d.MAR.cov.35.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.35.cl.prop.men[,9])
d.MAR.cov.35.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.35.cl.prop.women[,9])


# Cov 40


# Vector


vector.MAR.cov.40.cl.prop.men15.25.F.15.25 <- d.MAR.cov.40.cl.prop.men[,1]
vector.MAR.cov.40.cl.prop.women15.25.M.15.25 <- d.MAR.cov.40.cl.prop.women[,1]

vector.MAR.cov.40.cl.prop.men25.40.F.15.25 <- d.MAR.cov.40.cl.prop.men[,2]
vector.MAR.cov.40.cl.prop.women25.40.M.15.25 <- d.MAR.cov.40.cl.prop.women[,2]

vector.MAR.cov.40.cl.prop.men40.50.F.15.25 <- d.MAR.cov.40.cl.prop.men[,3]
vector.MAR.cov.40.cl.prop.women40.50.M.15.25 <- d.MAR.cov.40.cl.prop.women[,3]

vector.MAR.cov.40.cl.prop.men15.25.F.25.40 <- d.MAR.cov.40.cl.prop.men[,4]
vector.MAR.cov.40.cl.prop.women15.25.M.25.40 <- d.MAR.cov.40.cl.prop.women[,4]

vector.MAR.cov.40.cl.prop.men25.40.F.25.40 <- d.MAR.cov.40.cl.prop.men[,5]
vector.MAR.cov.40.cl.prop.women25.40.M.25.40 <- d.MAR.cov.40.cl.prop.women[,5]

vector.MAR.cov.40.cl.prop.men40.50.F.25.40 <- d.MAR.cov.40.cl.prop.men[,6]
vector.MAR.cov.40.cl.prop.women40.50.M.25.40 <- d.MAR.cov.40.cl.prop.women[,6]


vector.MAR.cov.40.cl.prop.men15.25.F.40.50 <- d.MAR.cov.40.cl.prop.men[,7]
vector.MAR.cov.40.cl.prop.women15.25.M.40.50 <- d.MAR.cov.40.cl.prop.women[,7]

vector.MAR.cov.40.cl.prop.men25.40.F.40.50 <- d.MAR.cov.40.cl.prop.men[,8]
vector.MAR.cov.40.cl.prop.women25.40.M.40.50 <- d.MAR.cov.40.cl.prop.women[,8]

vector.MAR.cov.40.cl.prop.men40.50.F.40.50 <- d.MAR.cov.40.cl.prop.men[,9]
vector.MAR.cov.40.cl.prop.women40.50.M.40.50 <- d.MAR.cov.40.cl.prop.women[,9]



# Summarised

d.MAR.cov.40.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.40.cl.prop.men[,1])
d.MAR.cov.40.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.40.cl.prop.women[,1])

d.MAR.cov.40.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.40.cl.prop.men[,2])
d.MAR.cov.40.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.40.cl.prop.women[,2])

d.MAR.cov.40.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.40.cl.prop.men[,3])
d.MAR.cov.40.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.40.cl.prop.women[,3])

d.MAR.cov.40.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.40.cl.prop.men[,4])
d.MAR.cov.40.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.40.cl.prop.women[,4])

d.MAR.cov.40.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.40.cl.prop.men[,5])
d.MAR.cov.40.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.40.cl.prop.women[,5])

d.MAR.cov.40.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.40.cl.prop.men[,6])
d.MAR.cov.40.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.40.cl.prop.women[,6])


d.MAR.cov.40.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.40.cl.prop.men[,7])
d.MAR.cov.40.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.40.cl.prop.women[,7])

d.MAR.cov.40.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.40.cl.prop.men[,8])
d.MAR.cov.40.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.40.cl.prop.women[,8])

d.MAR.cov.40.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.40.cl.prop.men[,9])
d.MAR.cov.40.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.40.cl.prop.women[,9])


# Cov 45


# Vector


vector.MAR.cov.45.cl.prop.men15.25.F.15.25 <- d.MAR.cov.45.cl.prop.men[,1]
vector.MAR.cov.45.cl.prop.women15.25.M.15.25 <- d.MAR.cov.45.cl.prop.women[,1]

vector.MAR.cov.45.cl.prop.men25.40.F.15.25 <- d.MAR.cov.45.cl.prop.men[,2]
vector.MAR.cov.45.cl.prop.women25.40.M.15.25 <- d.MAR.cov.45.cl.prop.women[,2]

vector.MAR.cov.45.cl.prop.men40.50.F.15.25 <- d.MAR.cov.45.cl.prop.men[,3]
vector.MAR.cov.45.cl.prop.women40.50.M.15.25 <- d.MAR.cov.45.cl.prop.women[,3]

vector.MAR.cov.45.cl.prop.men15.25.F.25.40 <- d.MAR.cov.45.cl.prop.men[,4]
vector.MAR.cov.45.cl.prop.women15.25.M.25.40 <- d.MAR.cov.45.cl.prop.women[,4]

vector.MAR.cov.45.cl.prop.men25.40.F.25.40 <- d.MAR.cov.45.cl.prop.men[,5]
vector.MAR.cov.45.cl.prop.women25.40.M.25.40 <- d.MAR.cov.45.cl.prop.women[,5]

vector.MAR.cov.45.cl.prop.men40.50.F.25.40 <- d.MAR.cov.45.cl.prop.men[,6]
vector.MAR.cov.45.cl.prop.women40.50.M.25.40 <- d.MAR.cov.45.cl.prop.women[,6]


vector.MAR.cov.45.cl.prop.men15.25.F.40.50 <- d.MAR.cov.45.cl.prop.men[,7]
vector.MAR.cov.45.cl.prop.women15.25.M.40.50 <- d.MAR.cov.45.cl.prop.women[,7]

vector.MAR.cov.45.cl.prop.men25.40.F.40.50 <- d.MAR.cov.45.cl.prop.men[,8]
vector.MAR.cov.45.cl.prop.women25.40.M.40.50 <- d.MAR.cov.45.cl.prop.women[,8]

vector.MAR.cov.45.cl.prop.men40.50.F.40.50 <- d.MAR.cov.45.cl.prop.men[,9]
vector.MAR.cov.45.cl.prop.women40.50.M.40.50 <- d.MAR.cov.45.cl.prop.women[,9]



# Summarised

d.MAR.cov.45.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.45.cl.prop.men[,1])
d.MAR.cov.45.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.45.cl.prop.women[,1])

d.MAR.cov.45.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.45.cl.prop.men[,2])
d.MAR.cov.45.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.45.cl.prop.women[,2])

d.MAR.cov.45.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.45.cl.prop.men[,3])
d.MAR.cov.45.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.45.cl.prop.women[,3])

d.MAR.cov.45.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.45.cl.prop.men[,4])
d.MAR.cov.45.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.45.cl.prop.women[,4])

d.MAR.cov.45.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.45.cl.prop.men[,5])
d.MAR.cov.45.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.45.cl.prop.women[,5])

d.MAR.cov.45.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.45.cl.prop.men[,6])
d.MAR.cov.45.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.45.cl.prop.women[,6])


d.MAR.cov.45.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.45.cl.prop.men[,7])
d.MAR.cov.45.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.45.cl.prop.women[,7])

d.MAR.cov.45.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.45.cl.prop.men[,8])
d.MAR.cov.45.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.45.cl.prop.women[,8])

d.MAR.cov.45.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.45.cl.prop.men[,9])
d.MAR.cov.45.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.45.cl.prop.women[,9])




# Cov 50


# Vector


vector.MAR.cov.50.cl.prop.men15.25.F.15.25 <- d.MAR.cov.50.cl.prop.men[,1]
vector.MAR.cov.50.cl.prop.women15.25.M.15.25 <- d.MAR.cov.50.cl.prop.women[,1]

vector.MAR.cov.50.cl.prop.men25.40.F.15.25 <- d.MAR.cov.50.cl.prop.men[,2]
vector.MAR.cov.50.cl.prop.women25.40.M.15.25 <- d.MAR.cov.50.cl.prop.women[,2]

vector.MAR.cov.50.cl.prop.men40.50.F.15.25 <- d.MAR.cov.50.cl.prop.men[,3]
vector.MAR.cov.50.cl.prop.women40.50.M.15.25 <- d.MAR.cov.50.cl.prop.women[,3]

vector.MAR.cov.50.cl.prop.men15.25.F.25.40 <- d.MAR.cov.50.cl.prop.men[,4]
vector.MAR.cov.50.cl.prop.women15.25.M.25.40 <- d.MAR.cov.50.cl.prop.women[,4]

vector.MAR.cov.50.cl.prop.men25.40.F.25.40 <- d.MAR.cov.50.cl.prop.men[,5]
vector.MAR.cov.50.cl.prop.women25.40.M.25.40 <- d.MAR.cov.50.cl.prop.women[,5]

vector.MAR.cov.50.cl.prop.men40.50.F.25.40 <- d.MAR.cov.50.cl.prop.men[,6]
vector.MAR.cov.50.cl.prop.women40.50.M.25.40 <- d.MAR.cov.50.cl.prop.women[,6]


vector.MAR.cov.50.cl.prop.men15.25.F.40.50 <- d.MAR.cov.50.cl.prop.men[,7]
vector.MAR.cov.50.cl.prop.women15.25.M.40.50 <- d.MAR.cov.50.cl.prop.women[,7]

vector.MAR.cov.50.cl.prop.men25.40.F.40.50 <- d.MAR.cov.50.cl.prop.men[,8]
vector.MAR.cov.50.cl.prop.women25.40.M.40.50 <- d.MAR.cov.50.cl.prop.women[,8]

vector.MAR.cov.50.cl.prop.men40.50.F.40.50 <- d.MAR.cov.50.cl.prop.men[,9]
vector.MAR.cov.50.cl.prop.women40.50.M.40.50 <- d.MAR.cov.50.cl.prop.women[,9]


# Summarised

d.MAR.cov.50.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.50.cl.prop.men[,1])
d.MAR.cov.50.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.50.cl.prop.women[,1])

d.MAR.cov.50.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.50.cl.prop.men[,2])
d.MAR.cov.50.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.50.cl.prop.women[,2])

d.MAR.cov.50.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.50.cl.prop.men[,3])
d.MAR.cov.50.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.50.cl.prop.women[,3])

d.MAR.cov.50.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.50.cl.prop.men[,4])
d.MAR.cov.50.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.50.cl.prop.women[,4])

d.MAR.cov.50.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.50.cl.prop.men[,5])
d.MAR.cov.50.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.50.cl.prop.women[,5])

d.MAR.cov.50.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.50.cl.prop.men[,6])
d.MAR.cov.50.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.50.cl.prop.women[,6])


d.MAR.cov.50.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.50.cl.prop.men[,7])
d.MAR.cov.50.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.50.cl.prop.women[,7])

d.MAR.cov.50.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.50.cl.prop.men[,8])
d.MAR.cov.50.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.50.cl.prop.women[,8])

d.MAR.cov.50.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.50.cl.prop.men[,9])
d.MAR.cov.50.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.50.cl.prop.women[,9])



# Cov 55


# Vector


vector.MAR.cov.55.cl.prop.men15.25.F.15.25 <- d.MAR.cov.55.cl.prop.men[,1]
vector.MAR.cov.55.cl.prop.women15.25.M.15.25 <- d.MAR.cov.55.cl.prop.women[,1]

vector.MAR.cov.55.cl.prop.men25.40.F.15.25 <- d.MAR.cov.55.cl.prop.men[,2]
vector.MAR.cov.55.cl.prop.women25.40.M.15.25 <- d.MAR.cov.55.cl.prop.women[,2]

vector.MAR.cov.55.cl.prop.men40.50.F.15.25 <- d.MAR.cov.55.cl.prop.men[,3]
vector.MAR.cov.55.cl.prop.women40.50.M.15.25 <- d.MAR.cov.55.cl.prop.women[,3]

vector.MAR.cov.55.cl.prop.men15.25.F.25.40 <- d.MAR.cov.55.cl.prop.men[,4]
vector.MAR.cov.55.cl.prop.women15.25.M.25.40 <- d.MAR.cov.55.cl.prop.women[,4]

vector.MAR.cov.55.cl.prop.men25.40.F.25.40 <- d.MAR.cov.55.cl.prop.men[,5]
vector.MAR.cov.55.cl.prop.women25.40.M.25.40 <- d.MAR.cov.55.cl.prop.women[,5]

vector.MAR.cov.55.cl.prop.men40.50.F.25.40 <- d.MAR.cov.55.cl.prop.men[,6]
vector.MAR.cov.55.cl.prop.women40.50.M.25.40 <- d.MAR.cov.55.cl.prop.women[,6]


vector.MAR.cov.55.cl.prop.men15.25.F.40.50 <- d.MAR.cov.55.cl.prop.men[,7]
vector.MAR.cov.55.cl.prop.women15.25.M.40.50 <- d.MAR.cov.55.cl.prop.women[,7]

vector.MAR.cov.55.cl.prop.men25.40.F.40.50 <- d.MAR.cov.55.cl.prop.men[,8]
vector.MAR.cov.55.cl.prop.women25.40.M.40.50 <- d.MAR.cov.55.cl.prop.women[,8]

vector.MAR.cov.55.cl.prop.men40.50.F.40.50 <- d.MAR.cov.55.cl.prop.men[,9]
vector.MAR.cov.55.cl.prop.women40.50.M.40.50 <- d.MAR.cov.55.cl.prop.women[,9]


# Summarised

d.MAR.cov.55.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.55.cl.prop.men[,1])
d.MAR.cov.55.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.55.cl.prop.women[,1])

d.MAR.cov.55.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.55.cl.prop.men[,2])
d.MAR.cov.55.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.55.cl.prop.women[,2])

d.MAR.cov.55.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.55.cl.prop.men[,3])
d.MAR.cov.55.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.55.cl.prop.women[,3])

d.MAR.cov.55.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.55.cl.prop.men[,4])
d.MAR.cov.55.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.55.cl.prop.women[,4])

d.MAR.cov.55.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.55.cl.prop.men[,5])
d.MAR.cov.55.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.55.cl.prop.women[,5])

d.MAR.cov.55.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.55.cl.prop.men[,6])
d.MAR.cov.55.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.55.cl.prop.women[,6])


d.MAR.cov.55.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.55.cl.prop.men[,7])
d.MAR.cov.55.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.55.cl.prop.women[,7])

d.MAR.cov.55.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.55.cl.prop.men[,8])
d.MAR.cov.55.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.55.cl.prop.women[,8])

d.MAR.cov.55.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.55.cl.prop.men[,9])
d.MAR.cov.55.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.55.cl.prop.women[,9])



# Cov 60


# Vector


vector.MAR.cov.60.cl.prop.men15.25.F.15.25 <- d.MAR.cov.60.cl.prop.men[,1]
vector.MAR.cov.60.cl.prop.women15.25.M.15.25 <- d.MAR.cov.60.cl.prop.women[,1]

vector.MAR.cov.60.cl.prop.men25.40.F.15.25 <- d.MAR.cov.60.cl.prop.men[,2]
vector.MAR.cov.60.cl.prop.women25.40.M.15.25 <- d.MAR.cov.60.cl.prop.women[,2]

vector.MAR.cov.60.cl.prop.men40.50.F.15.25 <- d.MAR.cov.60.cl.prop.men[,3]
vector.MAR.cov.60.cl.prop.women40.50.M.15.25 <- d.MAR.cov.60.cl.prop.women[,3]

vector.MAR.cov.60.cl.prop.men15.25.F.25.40 <- d.MAR.cov.60.cl.prop.men[,4]
vector.MAR.cov.60.cl.prop.women15.25.M.25.40 <- d.MAR.cov.60.cl.prop.women[,4]

vector.MAR.cov.60.cl.prop.men25.40.F.25.40 <- d.MAR.cov.60.cl.prop.men[,5]
vector.MAR.cov.60.cl.prop.women25.40.M.25.40 <- d.MAR.cov.60.cl.prop.women[,5]

vector.MAR.cov.60.cl.prop.men40.50.F.25.40 <- d.MAR.cov.60.cl.prop.men[,6]
vector.MAR.cov.60.cl.prop.women40.50.M.25.40 <- d.MAR.cov.60.cl.prop.women[,6]


vector.MAR.cov.60.cl.prop.men15.25.F.40.50 <- d.MAR.cov.60.cl.prop.men[,7]
vector.MAR.cov.60.cl.prop.women15.25.M.40.50 <- d.MAR.cov.60.cl.prop.women[,7]

vector.MAR.cov.60.cl.prop.men25.40.F.40.50 <- d.MAR.cov.60.cl.prop.men[,8]
vector.MAR.cov.60.cl.prop.women25.40.M.40.50 <- d.MAR.cov.60.cl.prop.women[,8]

vector.MAR.cov.60.cl.prop.men40.50.F.40.50 <- d.MAR.cov.60.cl.prop.men[,9]
vector.MAR.cov.60.cl.prop.women40.50.M.40.50 <- d.MAR.cov.60.cl.prop.women[,9]


# Summarised

d.MAR.cov.60.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.60.cl.prop.men[,1])
d.MAR.cov.60.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.60.cl.prop.women[,1])

d.MAR.cov.60.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.60.cl.prop.men[,2])
d.MAR.cov.60.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.60.cl.prop.women[,2])

d.MAR.cov.60.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.60.cl.prop.men[,3])
d.MAR.cov.60.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.60.cl.prop.women[,3])

d.MAR.cov.60.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.60.cl.prop.men[,4])
d.MAR.cov.60.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.60.cl.prop.women[,4])

d.MAR.cov.60.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.60.cl.prop.men[,5])
d.MAR.cov.60.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.60.cl.prop.women[,5])

d.MAR.cov.60.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.60.cl.prop.men[,6])
d.MAR.cov.60.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.60.cl.prop.women[,6])


d.MAR.cov.60.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.60.cl.prop.men[,7])
d.MAR.cov.60.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.60.cl.prop.women[,7])

d.MAR.cov.60.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.60.cl.prop.men[,8])
d.MAR.cov.60.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.60.cl.prop.women[,8])

d.MAR.cov.60.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.60.cl.prop.men[,9])
d.MAR.cov.60.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.60.cl.prop.women[,9])



# Cov 65


# Vector


vector.MAR.cov.65.cl.prop.men15.25.F.15.25 <- d.MAR.cov.65.cl.prop.men[,1]
vector.MAR.cov.65.cl.prop.women15.25.M.15.25 <- d.MAR.cov.65.cl.prop.women[,1]

vector.MAR.cov.65.cl.prop.men25.40.F.15.25 <- d.MAR.cov.65.cl.prop.men[,2]
vector.MAR.cov.65.cl.prop.women25.40.M.15.25 <- d.MAR.cov.65.cl.prop.women[,2]

vector.MAR.cov.65.cl.prop.men40.50.F.15.25 <- d.MAR.cov.65.cl.prop.men[,3]
vector.MAR.cov.65.cl.prop.women40.50.M.15.25 <- d.MAR.cov.65.cl.prop.women[,3]

vector.MAR.cov.65.cl.prop.men15.25.F.25.40 <- d.MAR.cov.65.cl.prop.men[,4]
vector.MAR.cov.65.cl.prop.women15.25.M.25.40 <- d.MAR.cov.65.cl.prop.women[,4]

vector.MAR.cov.65.cl.prop.men25.40.F.25.40 <- d.MAR.cov.65.cl.prop.men[,5]
vector.MAR.cov.65.cl.prop.women25.40.M.25.40 <- d.MAR.cov.65.cl.prop.women[,5]

vector.MAR.cov.65.cl.prop.men40.50.F.25.40 <- d.MAR.cov.65.cl.prop.men[,6]
vector.MAR.cov.65.cl.prop.women40.50.M.25.40 <- d.MAR.cov.65.cl.prop.women[,6]


vector.MAR.cov.65.cl.prop.men15.25.F.40.50 <- d.MAR.cov.65.cl.prop.men[,7]
vector.MAR.cov.65.cl.prop.women15.25.M.40.50 <- d.MAR.cov.65.cl.prop.women[,7]

vector.MAR.cov.65.cl.prop.men25.40.F.40.50 <- d.MAR.cov.65.cl.prop.men[,8]
vector.MAR.cov.65.cl.prop.women25.40.M.40.50 <- d.MAR.cov.65.cl.prop.women[,8]

vector.MAR.cov.65.cl.prop.men40.50.F.40.50 <- d.MAR.cov.65.cl.prop.men[,9]
vector.MAR.cov.65.cl.prop.women40.50.M.40.50 <- d.MAR.cov.65.cl.prop.women[,9]



# Summarised

d.MAR.cov.65.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.65.cl.prop.men[,1])
d.MAR.cov.65.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.65.cl.prop.women[,1])

d.MAR.cov.65.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.65.cl.prop.men[,2])
d.MAR.cov.65.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.65.cl.prop.women[,2])

d.MAR.cov.65.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.65.cl.prop.men[,3])
d.MAR.cov.65.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.65.cl.prop.women[,3])

d.MAR.cov.65.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.65.cl.prop.men[,4])
d.MAR.cov.65.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.65.cl.prop.women[,4])

d.MAR.cov.65.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.65.cl.prop.men[,5])
d.MAR.cov.65.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.65.cl.prop.women[,5])

d.MAR.cov.65.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.65.cl.prop.men[,6])
d.MAR.cov.65.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.65.cl.prop.women[,6])


d.MAR.cov.65.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.65.cl.prop.men[,7])
d.MAR.cov.65.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.65.cl.prop.women[,7])

d.MAR.cov.65.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.65.cl.prop.men[,8])
d.MAR.cov.65.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.65.cl.prop.women[,8])

d.MAR.cov.65.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.65.cl.prop.men[,9])
d.MAR.cov.65.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.65.cl.prop.women[,9])


# Cov 70


# Vector


vector.MAR.cov.70.cl.prop.men15.25.F.15.25 <- d.MAR.cov.70.cl.prop.men[,1]
vector.MAR.cov.70.cl.prop.women15.25.M.15.25 <- d.MAR.cov.70.cl.prop.women[,1]

vector.MAR.cov.70.cl.prop.men25.40.F.15.25 <- d.MAR.cov.70.cl.prop.men[,2]
vector.MAR.cov.70.cl.prop.women25.40.M.15.25 <- d.MAR.cov.70.cl.prop.women[,2]

vector.MAR.cov.70.cl.prop.men40.50.F.15.25 <- d.MAR.cov.70.cl.prop.men[,3]
vector.MAR.cov.70.cl.prop.women40.50.M.15.25 <- d.MAR.cov.70.cl.prop.women[,3]

vector.MAR.cov.70.cl.prop.men15.25.F.25.40 <- d.MAR.cov.70.cl.prop.men[,4]
vector.MAR.cov.70.cl.prop.women15.25.M.25.40 <- d.MAR.cov.70.cl.prop.women[,4]

vector.MAR.cov.70.cl.prop.men25.40.F.25.40 <- d.MAR.cov.70.cl.prop.men[,5]
vector.MAR.cov.70.cl.prop.women25.40.M.25.40 <- d.MAR.cov.70.cl.prop.women[,5]

vector.MAR.cov.70.cl.prop.men40.50.F.25.40 <- d.MAR.cov.70.cl.prop.men[,6]
vector.MAR.cov.70.cl.prop.women40.50.M.25.40 <- d.MAR.cov.70.cl.prop.women[,6]


vector.MAR.cov.70.cl.prop.men15.25.F.40.50 <- d.MAR.cov.70.cl.prop.men[,7]
vector.MAR.cov.70.cl.prop.women15.25.M.40.50 <- d.MAR.cov.70.cl.prop.women[,7]

vector.MAR.cov.70.cl.prop.men25.40.F.40.50 <- d.MAR.cov.70.cl.prop.men[,8]
vector.MAR.cov.70.cl.prop.women25.40.M.40.50 <- d.MAR.cov.70.cl.prop.women[,8]

vector.MAR.cov.70.cl.prop.men40.50.F.40.50 <- d.MAR.cov.70.cl.prop.men[,9]
vector.MAR.cov.70.cl.prop.women40.50.M.40.50 <- d.MAR.cov.70.cl.prop.women[,9]



# Summarised

d.MAR.cov.70.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.70.cl.prop.men[,1])
d.MAR.cov.70.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.70.cl.prop.women[,1])

d.MAR.cov.70.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.70.cl.prop.men[,2])
d.MAR.cov.70.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.70.cl.prop.women[,2])

d.MAR.cov.70.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.70.cl.prop.men[,3])
d.MAR.cov.70.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.70.cl.prop.women[,3])

d.MAR.cov.70.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.70.cl.prop.men[,4])
d.MAR.cov.70.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.70.cl.prop.women[,4])

d.MAR.cov.70.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.70.cl.prop.men[,5])
d.MAR.cov.70.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.70.cl.prop.women[,5])

d.MAR.cov.70.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.70.cl.prop.men[,6])
d.MAR.cov.70.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.70.cl.prop.women[,6])


d.MAR.cov.70.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.70.cl.prop.men[,7])
d.MAR.cov.70.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.70.cl.prop.women[,7])

d.MAR.cov.70.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.70.cl.prop.men[,8])
d.MAR.cov.70.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.70.cl.prop.women[,8])

d.MAR.cov.70.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.70.cl.prop.men[,9])

d.MAR.cov.70.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.70.cl.prop.women[,9])



# Cov 75


# Vector


vector.MAR.cov.75.cl.prop.men15.25.F.15.25 <- d.MAR.cov.75.cl.prop.men[,1]
vector.MAR.cov.75.cl.prop.women15.25.M.15.25 <- d.MAR.cov.75.cl.prop.women[,1]

vector.MAR.cov.75.cl.prop.men25.40.F.15.25 <- d.MAR.cov.75.cl.prop.men[,2]
vector.MAR.cov.75.cl.prop.women25.40.M.15.25 <- d.MAR.cov.75.cl.prop.women[,2]

vector.MAR.cov.75.cl.prop.men40.50.F.15.25 <- d.MAR.cov.75.cl.prop.men[,3]
vector.MAR.cov.75.cl.prop.women40.50.M.15.25 <- d.MAR.cov.75.cl.prop.women[,3]

vector.MAR.cov.75.cl.prop.men15.25.F.25.40 <- d.MAR.cov.75.cl.prop.men[,4]
vector.MAR.cov.75.cl.prop.women15.25.M.25.40 <- d.MAR.cov.75.cl.prop.women[,4]

vector.MAR.cov.75.cl.prop.men25.40.F.25.40 <- d.MAR.cov.75.cl.prop.men[,5]
vector.MAR.cov.75.cl.prop.women25.40.M.25.40 <- d.MAR.cov.75.cl.prop.women[,5]

vector.MAR.cov.75.cl.prop.men40.50.F.25.40 <- d.MAR.cov.75.cl.prop.men[,6]
vector.MAR.cov.75.cl.prop.women40.50.M.25.40 <- d.MAR.cov.75.cl.prop.women[,6]


vector.MAR.cov.75.cl.prop.men15.25.F.40.50 <- d.MAR.cov.75.cl.prop.men[,7]
vector.MAR.cov.75.cl.prop.women15.25.M.40.50 <- d.MAR.cov.75.cl.prop.women[,7]

vector.MAR.cov.75.cl.prop.men25.40.F.40.50 <- d.MAR.cov.75.cl.prop.men[,8]
vector.MAR.cov.75.cl.prop.women25.40.M.40.50 <- d.MAR.cov.75.cl.prop.women[,8]

vector.MAR.cov.75.cl.prop.men40.50.F.40.50 <- d.MAR.cov.75.cl.prop.men[,9]
vector.MAR.cov.75.cl.prop.women40.50.M.40.50 <- d.MAR.cov.75.cl.prop.women[,9]



# Summarised

d.MAR.cov.75.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.75.cl.prop.men[,1])
d.MAR.cov.75.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.75.cl.prop.women[,1])

d.MAR.cov.75.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.75.cl.prop.men[,2])
d.MAR.cov.75.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.75.cl.prop.women[,2])

d.MAR.cov.75.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.75.cl.prop.men[,3])
d.MAR.cov.75.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.75.cl.prop.women[,3])

d.MAR.cov.75.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.75.cl.prop.men[,4])
d.MAR.cov.75.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.75.cl.prop.women[,4])

d.MAR.cov.75.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.75.cl.prop.men[,5])
d.MAR.cov.75.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.75.cl.prop.women[,5])

d.MAR.cov.75.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.75.cl.prop.men[,6])
d.MAR.cov.75.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.75.cl.prop.women[,6])


d.MAR.cov.75.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.75.cl.prop.men[,7])
d.MAR.cov.75.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.75.cl.prop.women[,7])

d.MAR.cov.75.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.75.cl.prop.men[,8])
d.MAR.cov.75.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.75.cl.prop.women[,8])

d.MAR.cov.75.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.75.cl.prop.men[,9])
d.MAR.cov.75.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.75.cl.prop.women[,9])



# Cov 80


# Vector


vector.MAR.cov.80.cl.prop.men15.25.F.15.25 <- d.MAR.cov.80.cl.prop.men[,1]
vector.MAR.cov.80.cl.prop.women15.25.M.15.25 <- d.MAR.cov.80.cl.prop.women[,1]

vector.MAR.cov.80.cl.prop.men25.40.F.15.25 <- d.MAR.cov.80.cl.prop.men[,2]
vector.MAR.cov.80.cl.prop.women25.40.M.15.25 <- d.MAR.cov.80.cl.prop.women[,2]

vector.MAR.cov.80.cl.prop.men40.50.F.15.25 <- d.MAR.cov.80.cl.prop.men[,3]
vector.MAR.cov.80.cl.prop.women40.50.M.15.25 <- d.MAR.cov.80.cl.prop.women[,3]

vector.MAR.cov.80.cl.prop.men15.25.F.25.40 <- d.MAR.cov.80.cl.prop.men[,4]
vector.MAR.cov.80.cl.prop.women15.25.M.25.40 <- d.MAR.cov.80.cl.prop.women[,4]

vector.MAR.cov.80.cl.prop.men25.40.F.25.40 <- d.MAR.cov.80.cl.prop.men[,5]
vector.MAR.cov.80.cl.prop.women25.40.M.25.40 <- d.MAR.cov.80.cl.prop.women[,5]

vector.MAR.cov.80.cl.prop.men40.50.F.25.40 <- d.MAR.cov.80.cl.prop.men[,6]
vector.MAR.cov.80.cl.prop.women40.50.M.25.40 <- d.MAR.cov.80.cl.prop.women[,6]


vector.MAR.cov.80.cl.prop.men15.25.F.40.50 <- d.MAR.cov.80.cl.prop.men[,7]
vector.MAR.cov.80.cl.prop.women15.25.M.40.50 <- d.MAR.cov.80.cl.prop.women[,7]

vector.MAR.cov.80.cl.prop.men25.40.F.40.50 <- d.MAR.cov.80.cl.prop.men[,8]
vector.MAR.cov.80.cl.prop.women25.40.M.40.50 <- d.MAR.cov.80.cl.prop.women[,8]

vector.MAR.cov.80.cl.prop.men40.50.F.40.50 <- d.MAR.cov.80.cl.prop.men[,9]
vector.MAR.cov.80.cl.prop.women40.50.M.40.50 <- d.MAR.cov.80.cl.prop.women[,9]



# Summarised

d.MAR.cov.80.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.80.cl.prop.men[,1])
d.MAR.cov.80.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.80.cl.prop.women[,1])

d.MAR.cov.80.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.80.cl.prop.men[,2])
d.MAR.cov.80.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.80.cl.prop.women[,2])

d.MAR.cov.80.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.80.cl.prop.men[,3])
d.MAR.cov.80.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.80.cl.prop.women[,3])

d.MAR.cov.80.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.80.cl.prop.men[,4])
d.MAR.cov.80.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.80.cl.prop.women[,4])

d.MAR.cov.80.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.80.cl.prop.men[,5])
d.MAR.cov.80.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.80.cl.prop.women[,5])

d.MAR.cov.80.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.80.cl.prop.men[,6])
d.MAR.cov.80.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.80.cl.prop.women[,6])


d.MAR.cov.80.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.80.cl.prop.men[,7])
d.MAR.cov.80.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.80.cl.prop.women[,7])

d.MAR.cov.80.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.80.cl.prop.men[,8])
d.MAR.cov.80.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.80.cl.prop.women[,8])

d.MAR.cov.80.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.80.cl.prop.men[,9])
d.MAR.cov.80.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.80.cl.prop.women[,9])



# Cov 85


# Vector


vector.MAR.cov.85.cl.prop.men15.25.F.15.25 <- d.MAR.cov.85.cl.prop.men[,1]
vector.MAR.cov.85.cl.prop.women15.25.M.15.25 <- d.MAR.cov.85.cl.prop.women[,1]

vector.MAR.cov.85.cl.prop.men25.40.F.15.25 <- d.MAR.cov.85.cl.prop.men[,2]
vector.MAR.cov.85.cl.prop.women25.40.M.15.25 <- d.MAR.cov.85.cl.prop.women[,2]

vector.MAR.cov.85.cl.prop.men40.50.F.15.25 <- d.MAR.cov.85.cl.prop.men[,3]
vector.MAR.cov.85.cl.prop.women40.50.M.15.25 <- d.MAR.cov.85.cl.prop.women[,3]

vector.MAR.cov.85.cl.prop.men15.25.F.25.40 <- d.MAR.cov.85.cl.prop.men[,4]
vector.MAR.cov.85.cl.prop.women15.25.M.25.40 <- d.MAR.cov.85.cl.prop.women[,4]

vector.MAR.cov.85.cl.prop.men25.40.F.25.40 <- d.MAR.cov.85.cl.prop.men[,5]
vector.MAR.cov.85.cl.prop.women25.40.M.25.40 <- d.MAR.cov.85.cl.prop.women[,5]

vector.MAR.cov.85.cl.prop.men40.50.F.25.40 <- d.MAR.cov.85.cl.prop.men[,6]
vector.MAR.cov.85.cl.prop.women40.50.M.25.40 <- d.MAR.cov.85.cl.prop.women[,6]


vector.MAR.cov.85.cl.prop.men15.25.F.40.50 <- d.MAR.cov.85.cl.prop.men[,7]
vector.MAR.cov.85.cl.prop.women15.25.M.40.50 <- d.MAR.cov.85.cl.prop.women[,7]

vector.MAR.cov.85.cl.prop.men25.40.F.40.50 <- d.MAR.cov.85.cl.prop.men[,8]
vector.MAR.cov.85.cl.prop.women25.40.M.40.50 <- d.MAR.cov.85.cl.prop.women[,8]

vector.MAR.cov.85.cl.prop.men40.50.F.40.50 <- d.MAR.cov.85.cl.prop.men[,9]
vector.MAR.cov.85.cl.prop.women40.50.M.40.50 <- d.MAR.cov.85.cl.prop.women[,9]



# Summarised

d.MAR.cov.85.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.85.cl.prop.men[,1])
d.MAR.cov.85.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.85.cl.prop.women[,1])

d.MAR.cov.85.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.85.cl.prop.men[,2])
d.MAR.cov.85.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.85.cl.prop.women[,2])

d.MAR.cov.85.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.85.cl.prop.men[,3])
d.MAR.cov.85.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.85.cl.prop.women[,3])

d.MAR.cov.85.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.85.cl.prop.men[,4])
d.MAR.cov.85.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.85.cl.prop.women[,4])

d.MAR.cov.85.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.85.cl.prop.men[,5])
d.MAR.cov.85.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.85.cl.prop.women[,5])

d.MAR.cov.85.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.85.cl.prop.men[,6])
d.MAR.cov.85.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.85.cl.prop.women[,6])


d.MAR.cov.85.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.85.cl.prop.men[,7])
d.MAR.cov.85.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.85.cl.prop.women[,7])

d.MAR.cov.85.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.85.cl.prop.men[,8])
d.MAR.cov.85.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.85.cl.prop.women[,8])

d.MAR.cov.85.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.85.cl.prop.men[,9])
d.MAR.cov.85.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.85.cl.prop.women[,9])



# Cov 90


# Vector


vector.MAR.cov.90.cl.prop.men15.25.F.15.25 <- d.MAR.cov.90.cl.prop.men[,1]
vector.MAR.cov.90.cl.prop.women15.25.M.15.25 <- d.MAR.cov.90.cl.prop.women[,1]

vector.MAR.cov.90.cl.prop.men25.40.F.15.25 <- d.MAR.cov.90.cl.prop.men[,2]
vector.MAR.cov.90.cl.prop.women25.40.M.15.25 <- d.MAR.cov.90.cl.prop.women[,2]

vector.MAR.cov.90.cl.prop.men40.50.F.15.25 <- d.MAR.cov.90.cl.prop.men[,3]
vector.MAR.cov.90.cl.prop.women40.50.M.15.25 <- d.MAR.cov.90.cl.prop.women[,3]

vector.MAR.cov.90.cl.prop.men15.25.F.25.40 <- d.MAR.cov.90.cl.prop.men[,4]
vector.MAR.cov.90.cl.prop.women15.25.M.25.40 <- d.MAR.cov.90.cl.prop.women[,4]

vector.MAR.cov.90.cl.prop.men25.40.F.25.40 <- d.MAR.cov.90.cl.prop.men[,5]
vector.MAR.cov.90.cl.prop.women25.40.M.25.40 <- d.MAR.cov.90.cl.prop.women[,5]

vector.MAR.cov.90.cl.prop.men40.50.F.25.40 <- d.MAR.cov.90.cl.prop.men[,6]
vector.MAR.cov.90.cl.prop.women40.50.M.25.40 <- d.MAR.cov.90.cl.prop.women[,6]


vector.MAR.cov.90.cl.prop.men15.25.F.40.50 <- d.MAR.cov.90.cl.prop.men[,7]
vector.MAR.cov.90.cl.prop.women15.25.M.40.50 <- d.MAR.cov.90.cl.prop.women[,7]

vector.MAR.cov.90.cl.prop.men25.40.F.40.50 <- d.MAR.cov.90.cl.prop.men[,8]
vector.MAR.cov.90.cl.prop.women25.40.M.40.50 <- d.MAR.cov.90.cl.prop.women[,8]

vector.MAR.cov.90.cl.prop.men40.50.F.40.50 <- d.MAR.cov.90.cl.prop.men[,9]
vector.MAR.cov.90.cl.prop.women40.50.M.40.50 <- d.MAR.cov.90.cl.prop.women[,9]



# Summarised

d.MAR.cov.90.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.90.cl.prop.men[,1])
d.MAR.cov.90.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.90.cl.prop.women[,1])

d.MAR.cov.90.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.90.cl.prop.men[,2])
d.MAR.cov.90.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.90.cl.prop.women[,2])

d.MAR.cov.90.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.90.cl.prop.men[,3])
d.MAR.cov.90.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.90.cl.prop.women[,3])

d.MAR.cov.90.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.90.cl.prop.men[,4])
d.MAR.cov.90.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.90.cl.prop.women[,4])

d.MAR.cov.90.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.90.cl.prop.men[,5])
d.MAR.cov.90.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.90.cl.prop.women[,5])

d.MAR.cov.90.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.90.cl.prop.men[,6])
d.MAR.cov.90.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.90.cl.prop.women[,6])


d.MAR.cov.90.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.90.cl.prop.men[,7])
d.MAR.cov.90.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.90.cl.prop.women[,7])

d.MAR.cov.90.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.90.cl.prop.men[,8])
d.MAR.cov.90.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.90.cl.prop.women[,8])

d.MAR.cov.90.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.90.cl.prop.men[,9])
d.MAR.cov.90.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.90.cl.prop.women[,9])


# Cov 95


# Vector


vector.MAR.cov.95.cl.prop.men15.25.F.15.25 <- d.MAR.cov.95.cl.prop.men[,1]
vector.MAR.cov.95.cl.prop.women15.25.M.15.25 <- d.MAR.cov.95.cl.prop.women[,1]

vector.MAR.cov.95.cl.prop.men25.40.F.15.25 <- d.MAR.cov.95.cl.prop.men[,2]
vector.MAR.cov.95.cl.prop.women25.40.M.15.25 <- d.MAR.cov.95.cl.prop.women[,2]

vector.MAR.cov.95.cl.prop.men40.50.F.15.25 <- d.MAR.cov.95.cl.prop.men[,3]
vector.MAR.cov.95.cl.prop.women40.50.M.15.25 <- d.MAR.cov.95.cl.prop.women[,3]

vector.MAR.cov.95.cl.prop.men15.25.F.25.40 <- d.MAR.cov.95.cl.prop.men[,4]
vector.MAR.cov.95.cl.prop.women15.25.M.25.40 <- d.MAR.cov.95.cl.prop.women[,4]

vector.MAR.cov.95.cl.prop.men25.40.F.25.40 <- d.MAR.cov.95.cl.prop.men[,5]
vector.MAR.cov.95.cl.prop.women25.40.M.25.40 <- d.MAR.cov.95.cl.prop.women[,5]

vector.MAR.cov.95.cl.prop.men40.50.F.25.40 <- d.MAR.cov.95.cl.prop.men[,6]
vector.MAR.cov.95.cl.prop.women40.50.M.25.40 <- d.MAR.cov.95.cl.prop.women[,6]


vector.MAR.cov.95.cl.prop.men15.25.F.40.50 <- d.MAR.cov.95.cl.prop.men[,7]
vector.MAR.cov.95.cl.prop.women15.25.M.40.50 <- d.MAR.cov.95.cl.prop.women[,7]

vector.MAR.cov.95.cl.prop.men25.40.F.40.50 <- d.MAR.cov.95.cl.prop.men[,8]
vector.MAR.cov.95.cl.prop.women25.40.M.40.50 <- d.MAR.cov.95.cl.prop.women[,8]

vector.MAR.cov.95.cl.prop.men40.50.F.40.50 <- d.MAR.cov.95.cl.prop.men[,9]
vector.MAR.cov.95.cl.prop.women40.50.M.40.50 <- d.MAR.cov.95.cl.prop.women[,9]


# Summarised

d.MAR.cov.95.cl.prop.men15.25.F.15.25 <- quant.med(d.MAR.cov.95.cl.prop.men[,1])
d.MAR.cov.95.cl.prop.women15.25.M.15.25 <- quant.med(d.MAR.cov.95.cl.prop.women[,1])

d.MAR.cov.95.cl.prop.men25.40.F.15.25 <- quant.med(d.MAR.cov.95.cl.prop.men[,2])

d.MAR.cov.95.cl.prop.women25.40.M.15.25 <- quant.med(d.MAR.cov.95.cl.prop.women[,2])

d.MAR.cov.95.cl.prop.men40.50.F.15.25 <- quant.med(d.MAR.cov.95.cl.prop.men[,3])
d.MAR.cov.95.cl.prop.women40.50.M.15.25 <- quant.med(d.MAR.cov.95.cl.prop.women[,3])

d.MAR.cov.95.cl.prop.men15.25.F.25.40 <- quant.med(d.MAR.cov.95.cl.prop.men[,4])
d.MAR.cov.95.cl.prop.women15.25.M.25.40 <- quant.med(d.MAR.cov.95.cl.prop.women[,4])

d.MAR.cov.95.cl.prop.men25.40.F.25.40 <- quant.med(d.MAR.cov.95.cl.prop.men[,5])
d.MAR.cov.95.cl.prop.women25.40.M.25.40 <- quant.med(d.MAR.cov.95.cl.prop.women[,5])

d.MAR.cov.95.cl.prop.men40.50.F.25.40 <- quant.med(d.MAR.cov.95.cl.prop.men[,6])
d.MAR.cov.95.cl.prop.women40.50.M.25.40 <- quant.med(d.MAR.cov.95.cl.prop.women[,6])


d.MAR.cov.95.cl.prop.men15.25.F.40.50 <- quant.med(d.MAR.cov.95.cl.prop.men[,7])
d.MAR.cov.95.cl.prop.women15.25.M.40.50 <- quant.med(d.MAR.cov.95.cl.prop.women[,7])

d.MAR.cov.95.cl.prop.men25.40.F.40.50 <- quant.med(d.MAR.cov.95.cl.prop.men[,8])
d.MAR.cov.95.cl.prop.women25.40.M.40.50 <- quant.med(d.MAR.cov.95.cl.prop.women[,8])

d.MAR.cov.95.cl.prop.men40.50.F.40.50 <- quant.med(d.MAR.cov.95.cl.prop.men[,9])
d.MAR.cov.95.cl.prop.women40.50.M.40.50 <- quant.med(d.MAR.cov.95.cl.prop.women[,9])



prop_pairings_seq_cov <- matrix(c(d.MAR.cov.35.cl.prop.men15.25.F.15.25[2], d.MAR.cov.40.cl.prop.men15.25.F.15.25[2], 
                                  d.MAR.cov.45.cl.prop.men15.25.F.15.25[2], d.MAR.cov.50.cl.prop.men15.25.F.15.25[2], 
                                  d.MAR.cov.55.cl.prop.men15.25.F.15.25[2], d.MAR.cov.60.cl.prop.men15.25.F.15.25[2], 
                                  d.MAR.cov.65.cl.prop.men15.25.F.15.25[2], d.MAR.cov.70.cl.prop.men15.25.F.15.25[2], 
                                  d.MAR.cov.75.cl.prop.men15.25.F.15.25[2], d.MAR.cov.80.cl.prop.men15.25.F.15.25[2], 
                                  d.MAR.cov.85.cl.prop.men15.25.F.15.25[2], d.MAR.cov.90.cl.prop.men15.25.F.15.25[2], 
                                  d.MAR.cov.95.cl.prop.men15.25.F.15.25[2], d.MAR.true.cov.100.prop.men15.25.F.15.25[2],
                                  
                                  d.MAR.cov.35.cl.prop.women15.25.M.15.25[2], d.MAR.cov.40.cl.prop.women15.25.M.15.25[2], 
                                  d.MAR.cov.45.cl.prop.women15.25.M.15.25[2], d.MAR.cov.50.cl.prop.women15.25.M.15.25[2], 
                                  d.MAR.cov.55.cl.prop.women15.25.M.15.25[2], d.MAR.cov.60.cl.prop.women15.25.M.15.25[2], 
                                  d.MAR.cov.65.cl.prop.women15.25.M.15.25[2], d.MAR.cov.70.cl.prop.women15.25.M.15.25[2], 
                                  d.MAR.cov.75.cl.prop.women15.25.M.15.25[2], d.MAR.cov.80.cl.prop.women15.25.M.15.25[2], 
                                  d.MAR.cov.85.cl.prop.women15.25.M.15.25[2], d.MAR.cov.90.cl.prop.women15.25.M.15.25[2], 
                                  d.MAR.cov.95.cl.prop.women15.25.M.15.25[2], d.MAR.true.cov.100.prop.women15.25.M.15.25[2],
                                  
                                  d.MAR.cov.35.cl.prop.men25.40.F.15.25[2], d.MAR.cov.40.cl.prop.men25.40.F.15.25[2], 
                                  d.MAR.cov.45.cl.prop.men25.40.F.15.25[2], d.MAR.cov.50.cl.prop.men25.40.F.15.25[2], 
                                  d.MAR.cov.55.cl.prop.men25.40.F.15.25[2], d.MAR.cov.60.cl.prop.men25.40.F.15.25[2], 
                                  d.MAR.cov.65.cl.prop.men25.40.F.15.25[2], d.MAR.cov.70.cl.prop.men25.40.F.15.25[2], 
                                  d.MAR.cov.75.cl.prop.men25.40.F.15.25[2], d.MAR.cov.80.cl.prop.men25.40.F.15.25[2], 
                                  d.MAR.cov.85.cl.prop.men25.40.F.15.25[2], d.MAR.cov.90.cl.prop.men25.40.F.15.25[2], 
                                  d.MAR.cov.95.cl.prop.men25.40.F.15.25[2], d.MAR.true.cov.100.prop.men25.40.F.15.25[2],
                                  
                                  d.MAR.cov.35.cl.prop.women25.40.M.15.25[2], d.MAR.cov.40.cl.prop.women25.40.M.15.25[2], 
                                  d.MAR.cov.45.cl.prop.women25.40.M.15.25[2], d.MAR.cov.50.cl.prop.women25.40.M.15.25[2], 
                                  d.MAR.cov.55.cl.prop.women25.40.M.15.25[2], d.MAR.cov.60.cl.prop.women25.40.M.15.25[2], 
                                  d.MAR.cov.65.cl.prop.women25.40.M.15.25[2], d.MAR.cov.70.cl.prop.women25.40.M.15.25[2], 
                                  d.MAR.cov.75.cl.prop.women25.40.M.15.25[2], d.MAR.cov.80.cl.prop.women25.40.M.15.25[2], 
                                  d.MAR.cov.85.cl.prop.women25.40.M.15.25[2], d.MAR.cov.90.cl.prop.women25.40.M.15.25[2], 
                                  d.MAR.cov.95.cl.prop.women25.40.M.15.25[2], d.MAR.true.cov.100.prop.women25.40.M.15.25[2],
                                  
                                  d.MAR.cov.35.cl.prop.men40.50.F.15.25[2], d.MAR.cov.40.cl.prop.men40.50.F.15.25[2], 
                                  d.MAR.cov.45.cl.prop.men40.50.F.15.25[2], d.MAR.cov.50.cl.prop.men40.50.F.15.25[2], 
                                  d.MAR.cov.55.cl.prop.men40.50.F.15.25[2], d.MAR.cov.60.cl.prop.men40.50.F.15.25[2], 
                                  d.MAR.cov.65.cl.prop.men40.50.F.15.25[2], d.MAR.cov.70.cl.prop.men40.50.F.15.25[2], 
                                  d.MAR.cov.75.cl.prop.men40.50.F.15.25[2], d.MAR.cov.80.cl.prop.men40.50.F.15.25[2], 
                                  d.MAR.cov.85.cl.prop.men40.50.F.15.25[2], d.MAR.cov.90.cl.prop.men40.50.F.15.25[2], 
                                  d.MAR.cov.95.cl.prop.men40.50.F.15.25[2], d.MAR.true.cov.100.prop.men40.50.F.15.25[2],
                                  
                                  d.MAR.cov.35.cl.prop.women40.50.M.15.25[2], d.MAR.cov.40.cl.prop.women40.50.M.15.25[2], 
                                  d.MAR.cov.45.cl.prop.women40.50.M.15.25[2], d.MAR.cov.50.cl.prop.women40.50.M.15.25[2], 
                                  d.MAR.cov.55.cl.prop.women40.50.M.15.25[2], d.MAR.cov.60.cl.prop.women40.50.M.15.25[2], 
                                  d.MAR.cov.65.cl.prop.women40.50.M.15.25[2], d.MAR.cov.70.cl.prop.women40.50.M.15.25[2], 
                                  d.MAR.cov.75.cl.prop.women40.50.M.15.25[2], d.MAR.cov.80.cl.prop.women40.50.M.15.25[2], 
                                  d.MAR.cov.85.cl.prop.women40.50.M.15.25[2], d.MAR.cov.90.cl.prop.women40.50.M.15.25[2], 
                                  d.MAR.cov.95.cl.prop.women40.50.M.15.25[2], d.MAR.true.cov.100.prop.women40.50.M.15.25[2],
                                  
                                  d.MAR.cov.35.cl.prop.men15.25.F.25.40[2], d.MAR.cov.40.cl.prop.men15.25.F.25.40[2], 
                                  d.MAR.cov.45.cl.prop.men15.25.F.25.40[2], d.MAR.cov.50.cl.prop.men15.25.F.25.40[2], 
                                  d.MAR.cov.55.cl.prop.men15.25.F.25.40[2], d.MAR.cov.60.cl.prop.men15.25.F.25.40[2], 
                                  d.MAR.cov.65.cl.prop.men15.25.F.25.40[2], d.MAR.cov.70.cl.prop.men15.25.F.25.40[2], 
                                  d.MAR.cov.75.cl.prop.men15.25.F.25.40[2], d.MAR.cov.80.cl.prop.men15.25.F.25.40[2], 
                                  d.MAR.cov.85.cl.prop.men15.25.F.25.40[2], d.MAR.cov.90.cl.prop.men15.25.F.25.40[2], 
                                  d.MAR.cov.95.cl.prop.men15.25.F.25.40[2], d.MAR.true.cov.100.prop.men15.25.F.25.40[2],
                                  
                                  d.MAR.cov.35.cl.prop.women15.25.M.25.40[2], d.MAR.cov.40.cl.prop.women15.25.M.25.40[2], 
                                  d.MAR.cov.45.cl.prop.women15.25.M.25.40[2], d.MAR.cov.50.cl.prop.women15.25.M.25.40[2], 
                                  d.MAR.cov.55.cl.prop.women15.25.M.25.40[2], d.MAR.cov.60.cl.prop.women15.25.M.25.40[2], 
                                  d.MAR.cov.65.cl.prop.women15.25.M.25.40[2], d.MAR.cov.70.cl.prop.women15.25.M.25.40[2], 
                                  d.MAR.cov.75.cl.prop.women15.25.M.25.40[2], d.MAR.cov.80.cl.prop.women15.25.M.25.40[2], 
                                  d.MAR.cov.85.cl.prop.women15.25.M.25.40[2], d.MAR.cov.90.cl.prop.women15.25.M.25.40[2], 
                                  d.MAR.cov.95.cl.prop.women15.25.M.25.40[2], d.MAR.true.cov.100.prop.women15.25.M.25.40[2],
                                  
                                  d.MAR.cov.35.cl.prop.men25.40.F.25.40[2], d.MAR.cov.40.cl.prop.men25.40.F.25.40[2], 
                                  d.MAR.cov.45.cl.prop.men25.40.F.25.40[2], d.MAR.cov.50.cl.prop.men25.40.F.25.40[2], 
                                  d.MAR.cov.55.cl.prop.men25.40.F.25.40[2], d.MAR.cov.60.cl.prop.men25.40.F.25.40[2], 
                                  d.MAR.cov.65.cl.prop.men25.40.F.25.40[2], d.MAR.cov.70.cl.prop.men25.40.F.25.40[2], 
                                  d.MAR.cov.75.cl.prop.men25.40.F.25.40[2], d.MAR.cov.80.cl.prop.men25.40.F.25.40[2], 
                                  d.MAR.cov.85.cl.prop.men25.40.F.25.40[2], d.MAR.cov.90.cl.prop.men25.40.F.25.40[2], 
                                  d.MAR.cov.95.cl.prop.men25.40.F.25.40[2], d.MAR.true.cov.100.prop.men25.40.F.25.40[2],
                                  
                                  d.MAR.cov.35.cl.prop.women25.40.M.25.40[2], d.MAR.cov.40.cl.prop.women25.40.M.25.40[2], 
                                  d.MAR.cov.45.cl.prop.women25.40.M.25.40[2], d.MAR.cov.50.cl.prop.women25.40.M.25.40[2], 
                                  d.MAR.cov.55.cl.prop.women25.40.M.25.40[2], d.MAR.cov.60.cl.prop.women25.40.M.25.40[2], 
                                  d.MAR.cov.65.cl.prop.women25.40.M.25.40[2], d.MAR.cov.70.cl.prop.women25.40.M.25.40[2], 
                                  d.MAR.cov.75.cl.prop.women25.40.M.25.40[2], d.MAR.cov.80.cl.prop.women25.40.M.25.40[2], 
                                  d.MAR.cov.85.cl.prop.women25.40.M.25.40[2], d.MAR.cov.90.cl.prop.women25.40.M.25.40[2], 
                                  d.MAR.cov.95.cl.prop.women25.40.M.25.40[2], d.MAR.true.cov.100.prop.women25.40.M.25.40[2],
                                  
                                  
                                  d.MAR.cov.35.cl.prop.men40.50.F.25.40[2], d.MAR.cov.40.cl.prop.men40.50.F.25.40[2], 
                                  d.MAR.cov.45.cl.prop.men40.50.F.25.40[2], d.MAR.cov.50.cl.prop.men40.50.F.25.40[2], 
                                  d.MAR.cov.55.cl.prop.men40.50.F.25.40[2], d.MAR.cov.60.cl.prop.men40.50.F.25.40[2], 
                                  d.MAR.cov.65.cl.prop.men40.50.F.25.40[2], d.MAR.cov.70.cl.prop.men40.50.F.25.40[2], 
                                  d.MAR.cov.75.cl.prop.men40.50.F.25.40[2], d.MAR.cov.80.cl.prop.men40.50.F.25.40[2], 
                                  d.MAR.cov.85.cl.prop.men40.50.F.25.40[2], d.MAR.cov.90.cl.prop.men40.50.F.25.40[2], 
                                  d.MAR.cov.95.cl.prop.men40.50.F.25.40[2], d.MAR.true.cov.100.prop.men40.50.F.25.40[2],
                                  
                                  d.MAR.cov.35.cl.prop.women40.50.M.25.40[2], d.MAR.cov.40.cl.prop.women40.50.M.25.40[2], 
                                  d.MAR.cov.45.cl.prop.women40.50.M.25.40[2], d.MAR.cov.50.cl.prop.women40.50.M.25.40[2], 
                                  d.MAR.cov.55.cl.prop.women40.50.M.25.40[2], d.MAR.cov.60.cl.prop.women40.50.M.25.40[2], 
                                  d.MAR.cov.65.cl.prop.women40.50.M.25.40[2], d.MAR.cov.70.cl.prop.women40.50.M.25.40[2], 
                                  d.MAR.cov.75.cl.prop.women40.50.M.25.40[2], d.MAR.cov.80.cl.prop.women40.50.M.25.40[2], 
                                  d.MAR.cov.85.cl.prop.women40.50.M.25.40[2], d.MAR.cov.90.cl.prop.women40.50.M.25.40[2], 
                                  d.MAR.cov.95.cl.prop.women40.50.M.25.40[2], d.MAR.true.cov.100.prop.women40.50.M.25.40[2],
                                  
                                  d.MAR.cov.35.cl.prop.men15.25.F.40.50[2], d.MAR.cov.40.cl.prop.men15.25.F.40.50[2], 
                                  d.MAR.cov.45.cl.prop.men15.25.F.40.50[2], d.MAR.cov.50.cl.prop.men15.25.F.40.50[2], 
                                  d.MAR.cov.55.cl.prop.men15.25.F.40.50[2], d.MAR.cov.60.cl.prop.men15.25.F.40.50[2], 
                                  d.MAR.cov.65.cl.prop.men15.25.F.40.50[2], d.MAR.cov.70.cl.prop.men15.25.F.40.50[2], 
                                  d.MAR.cov.75.cl.prop.men15.25.F.40.50[2], d.MAR.cov.80.cl.prop.men15.25.F.40.50[2], 
                                  d.MAR.cov.85.cl.prop.men15.25.F.40.50[2], d.MAR.cov.90.cl.prop.men15.25.F.40.50[2], 
                                  d.MAR.cov.95.cl.prop.men15.25.F.40.50[2], d.MAR.true.cov.100.prop.men15.25.F.40.50[2],
                                  
                                  d.MAR.cov.35.cl.prop.women15.25.M.40.50[2], d.MAR.cov.40.cl.prop.women15.25.M.40.50[2], 
                                  d.MAR.cov.45.cl.prop.women15.25.M.40.50[2], d.MAR.cov.50.cl.prop.women15.25.M.40.50[2], 
                                  d.MAR.cov.55.cl.prop.women15.25.M.40.50[2], d.MAR.cov.60.cl.prop.women15.25.M.40.50[2], 
                                  d.MAR.cov.65.cl.prop.women15.25.M.40.50[2], d.MAR.cov.70.cl.prop.women15.25.M.40.50[2], 
                                  d.MAR.cov.75.cl.prop.women15.25.M.40.50[2], d.MAR.cov.80.cl.prop.women15.25.M.40.50[2], 
                                  d.MAR.cov.85.cl.prop.women15.25.M.40.50[2], d.MAR.cov.90.cl.prop.women15.25.M.40.50[2], 
                                  d.MAR.cov.95.cl.prop.women15.25.M.40.50[2], d.MAR.true.cov.100.prop.women15.25.M.40.50[2],
                                  
                                  d.MAR.cov.35.cl.prop.men25.40.F.40.50[2], d.MAR.cov.40.cl.prop.men25.40.F.40.50[2], 
                                  d.MAR.cov.45.cl.prop.men25.40.F.40.50[2], d.MAR.cov.50.cl.prop.men25.40.F.40.50[2], 
                                  d.MAR.cov.55.cl.prop.men25.40.F.40.50[2], d.MAR.cov.60.cl.prop.men25.40.F.40.50[2], 
                                  d.MAR.cov.65.cl.prop.men25.40.F.40.50[2], d.MAR.cov.70.cl.prop.men25.40.F.40.50[2], 
                                  d.MAR.cov.75.cl.prop.men25.40.F.40.50[2], d.MAR.cov.80.cl.prop.men25.40.F.40.50[2], 
                                  d.MAR.cov.85.cl.prop.men25.40.F.40.50[2], d.MAR.cov.90.cl.prop.men25.40.F.40.50[2], 
                                  d.MAR.cov.95.cl.prop.men25.40.F.40.50[2], d.MAR.true.cov.100.prop.men25.40.F.40.50[2],
                                  
                                  d.MAR.cov.35.cl.prop.women25.40.M.40.50[2], d.MAR.cov.40.cl.prop.women25.40.M.40.50[2], 
                                  d.MAR.cov.45.cl.prop.women25.40.M.40.50[2], d.MAR.cov.50.cl.prop.women25.40.M.40.50[2], 
                                  d.MAR.cov.55.cl.prop.women25.40.M.40.50[2], d.MAR.cov.60.cl.prop.women25.40.M.40.50[2], 
                                  d.MAR.cov.65.cl.prop.women25.40.M.40.50[2], d.MAR.cov.70.cl.prop.women25.40.M.40.50[2], 
                                  d.MAR.cov.75.cl.prop.women25.40.M.40.50[2], d.MAR.cov.80.cl.prop.women25.40.M.40.50[2], 
                                  d.MAR.cov.85.cl.prop.women25.40.M.40.50[2], d.MAR.cov.90.cl.prop.women25.40.M.40.50[2], 
                                  d.MAR.cov.95.cl.prop.women25.40.M.40.50[2], d.MAR.true.cov.100.prop.women25.40.M.40.50[2],
                                  
                                  d.MAR.cov.35.cl.prop.men40.50.F.40.50[2], d.MAR.cov.40.cl.prop.men40.50.F.40.50[2], 
                                  d.MAR.cov.45.cl.prop.men40.50.F.40.50[2], d.MAR.cov.50.cl.prop.men40.50.F.40.50[2], 
                                  d.MAR.cov.55.cl.prop.men40.50.F.40.50[2], d.MAR.cov.60.cl.prop.men40.50.F.40.50[2], 
                                  d.MAR.cov.65.cl.prop.men40.50.F.40.50[2], d.MAR.cov.70.cl.prop.men40.50.F.40.50[2], 
                                  d.MAR.cov.75.cl.prop.men40.50.F.40.50[2], d.MAR.cov.80.cl.prop.men40.50.F.40.50[2], 
                                  d.MAR.cov.85.cl.prop.men40.50.F.40.50[2], d.MAR.cov.90.cl.prop.men40.50.F.40.50[2], 
                                  d.MAR.cov.95.cl.prop.men40.50.F.40.50[2], d.MAR.true.cov.100.prop.men40.50.F.40.50[2],
                                  
                                  d.MAR.cov.35.cl.prop.women40.50.M.40.50[2], d.MAR.cov.40.cl.prop.women40.50.M.40.50[2], 
                                  d.MAR.cov.45.cl.prop.women40.50.M.40.50[2], d.MAR.cov.50.cl.prop.women40.50.M.40.50[2], 
                                  d.MAR.cov.55.cl.prop.women40.50.M.40.50[2], d.MAR.cov.60.cl.prop.women40.50.M.40.50[2], 
                                  d.MAR.cov.65.cl.prop.women40.50.M.40.50[2], d.MAR.cov.70.cl.prop.women40.50.M.40.50[2], 
                                  d.MAR.cov.75.cl.prop.women40.50.M.40.50[2], d.MAR.cov.80.cl.prop.women40.50.M.40.50[2], 
                                  d.MAR.cov.85.cl.prop.women40.50.M.40.50[2], d.MAR.cov.90.cl.prop.women40.50.M.40.50[2], 
                                  d.MAR.cov.95.cl.prop.women40.50.M.40.50[2], d.MAR.true.cov.100.prop.women40.50.M.40.50[2]
),

ncol = 14,
byrow = TRUE)

prop_pairings_seq_cov <- round(prop_pairings_seq_cov, digits = 3)




colnames(prop_pairings_seq_cov) <- c("35", "40", "45",
                                     "50", "55", "60",
                                     "65", "70", "75",
                                     "80", "85", "90",
                                     "95", "true_100")

rownames(prop_pairings_seq_cov) <-  c("M.15.25.F.15.25", "F.15.25.M.15.25", 
                                      "M.25.40.F.15.25", "F.25.40.M.15.25", 
                                      "M.40.50.F.15.25", "F.40.50.M.15.25",
                                      "M.15.25.F.25.40", "F.15.25.M.25.40",
                                      "M.25.40.F.25.40", "F.25.40.M.25.40",
                                      "M.40.50.F.25.40", "F.40.50.M.25.40",
                                      "M.15.25.F.40.50", "F.15.25.M.40.50",
                                      "M.25.40.F.40.50", "F.25.40.M.40.50",
                                      "M.40.50.F.40.50", "F.40.50.M.40.50") 

# prop_pairings_seq_cov %>% 
#   kable() %>% 
#   kable_styling("striped") # Commentred in OCTOBER


# Critical age groups

prop_pairings_seq_cov_target <- prop_pairings_seq_cov[-c(4, 6, 7, 12, 13, 15, 17, 18),]


prop_pairings_seq_cov_target %>%
  kable() %>%
  kable_styling("striped")
35 40 45 50 55 60 65 70 75 80 85 90 95 true_100
M.15.25.F.15.25 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
F.15.25.M.15.25 0.286 0.333 0.333 0.333 0.333 0.333 0.316 0.308 0.294 0.286 0.265 0.250 0.259 0.139
M.25.40.F.15.25 1.000 0.875 0.889 0.900 0.857 0.875 0.867 0.875 0.880 0.889 0.889 0.897 0.900 0.826
M.40.50.F.15.25 0.000 0.000 0.400 0.500 0.500 0.500 0.500 0.545 0.600 0.600 0.615 0.600 0.667 0.519
F.15.25.M.25.40 0.417 0.429 0.429 0.458 0.464 0.500 0.500 0.500 0.500 0.508 0.518 0.529 0.522 0.556
M.25.40.F.25.40 0.000 0.000 0.000 0.000 0.091 0.100 0.125 0.111 0.118 0.111 0.111 0.100 0.100 0.174
F.25.40.M.25.40 0.000 0.000 0.000 0.000 0.250 0.273 0.333 0.333 0.333 0.333 0.333 0.333 0.333 0.280
M.40.50.F.25.40 0.000 0.000 0.321 0.286 0.375 0.400 0.400 0.400 0.333 0.375 0.359 0.389 0.333 0.478
F.15.25.M.40.50 0.000 0.000 0.100 0.125 0.125 0.135 0.136 0.167 0.161 0.158 0.171 0.167 0.174 0.278
F.25.40.M.40.50 0.000 0.000 0.293 0.345 0.429 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.696
write.csv(prop_pairings_seq_cov_target, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_14_Proportions_Pairings_at_35_95_Coverage.csv")



colnames(prop_pairings_seq_cov_target) <- c("35", "40", "45", "50", "55", "60",       
                                            "65", "70", "75", "80", "85", "90",       
                                            "95", "true_100")

v <- data.frame(matrix(ncol = 5, nrow = 0))
x <- c("cov", "val", "param", "f_m", "age_groups")
colnames(v) <- x


d <- as.data.frame(prop_pairings_seq_cov_target)
d <- d[order(row.names(d)), ]
d_f <- d
d_f$pairs <- c(rep("Females_Males", nrow(d)/2), rep("Males_Females", nrow(d)/2))
d_f$age_grp <- c(rep(c("15_24 & 15_24", "15_24 & 25_39", "15_24 & 40_49", "25_39 & 25_39", "25_39 & 40_49"), 2))


for(i in 1:length(rownames(d_f))){
  
  cov_i <- names(d_f)[1:14]
  val_i <- as.numeric(d_f[i,][1:14])
  f_m_pairs_i <- rep(as.character(d_f[i,][15]), length(cov_i)) 
  param_i <- rep(rownames(d_f[i,]), length(cov_i))
  grp_i <- rep(as.character(d_f[i,][16]), length(cov_i)) 
  
  
  v_i <- data.frame(cov_i, val_i, param_i, f_m_pairs_i, grp_i)
  
  colnames(v_i) <- x
  
  v <- rbind(v, v_i)
}

saveRDS(v, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/MAR_a_9_Proportion_of_Pairings_at_35_95_Coverage.RDS")

plot.prop_pairings_seq_cov_target_v <- ggplot(v, aes(x=cov, y=val, colour= age_groups, group = age_groups)) + 
  geom_line(size=1) +
  geom_point() +
  facet_grid(. ~ f_m) + 
  # theme(legend.position="top")+
  xlab("Sampling Coverage (%)") + ylab("Proportion")

print(plot.prop_pairings_seq_cov_target_v)

ggsave(filename = "Plot_a_9_Proportion_of_Pairings_at_35_95_Coverage.pdf",
       plot = plot.prop_pairings_seq_cov_target_v,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 30, height = 15, units = "cm")






# "F.25.40.M.15.25", "F.40.50.M.15.25", "M.15.25.F.25.40", "F.40.50.M.25.40", 
# "M.15.25.F.40.50", "M.25.40.F.40.50", "M.40.50.F.40.50", "F.40.50.M.40.50"

4.3 Goodness of fit of proportions of pairings

Difference between inferred proportions of men/women from a certain age group in transmission clusters and true values of same age group proportion at 100% coverage, it shows how good is our inferrence from transmission clusters. For this purpose, we computed the Root Mean Squared Error (RMSE) of proportions of pairings in each sampling scenario:

\[\sqrt{mean[(V_{true_{100}} – V_{cov})^2]}\]

where \(V_{true_{100}}\) is a vector of true proportion values at 100%, and \(V_{cov}\) the proportion values at a given sampling scenario.

# Cov 35


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.35.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.35 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.35)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.35)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.35.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.35 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.35)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.35.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.35 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.35)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.35 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.35)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.35.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.35 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.35)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.35.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.35 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.35)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.35)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.35.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.35 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.35)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.35.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.35 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.35)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.35)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.35.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.35 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.35)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.35.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.35 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.35)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.35 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.35)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.35.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.35 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.35)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.35.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.35 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.35)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.35 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.35)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.35.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.35 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.35)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.35.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.35 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.35)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.35 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.35)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.35.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.35 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.35)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.35.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.35 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.35)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.35 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.35)
ARMSE.error.infer.clust.cov.100.women.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.35.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.35 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.35)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.35.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.35 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.35)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.35 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.35)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.35.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.35 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.35)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.35.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.35 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.35)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.35 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.35)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.35.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.35 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.35)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.35.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.35 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.35)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.35 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.35)
ARMSE.error.infer.clust.cov.100.women.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.35.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.35 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.35)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.35.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.35 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.35)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.35 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.35)
ARMSE.error.infer.clust.cov.100.women.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.35.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.35 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.35)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.35.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.35 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.35)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.35 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.35)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.35.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.35 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.35)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.35.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.35 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.35)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.35 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.35)
ARMSE.error.infer.clust.cov.100.women.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.35.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.35 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.35)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.35.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.35 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.35)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.35 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.35)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.35.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.35 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.35)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.35.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.35 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.35)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.35 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.35)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.35.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.35 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.35)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.35.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.35 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.35)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.35 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.35)
ARMSE.error.infer.clust.cov.100.women.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.35.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.35 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.35)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.35 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.35.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.35 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.35)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.35 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.35)
ARMSE.error.infer.clust.cov.100.women.cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.35.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.35 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.35)


# Cov 40


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.40.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.40 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.40)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.40)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.40.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.40 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.40)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.40.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.40 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.40)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.40 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.40)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.40.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.40 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.40)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.40.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.40 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.40)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.40)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.40.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.40 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.40)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.40.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.40 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.40)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.40)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.40.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.40 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.40)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.40.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.40 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.40)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.40 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.40)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.40.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.40 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.40)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.40.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.40 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.40)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.40 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.40)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.40.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.40 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.40)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.40.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.40 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.40)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.40 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.40)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.40.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.40 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.40)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.40.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.40 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.40)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.40 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.40)
ARMSE.error.infer.clust.cov.100.women.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.40.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.40 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.40)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.40.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.40 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.40)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.40 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.40)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.40.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.40 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.40)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.40.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.40 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.40)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.40 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.40)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.40.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.40 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.40)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.40.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.40 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.40)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.40 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.40)
ARMSE.error.infer.clust.cov.100.women.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.40.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.40 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.40)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.40.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.40 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.40)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.40 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.40)
ARMSE.error.infer.clust.cov.100.women.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.40.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.40 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.40)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.40.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.40 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.40)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.40 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.40)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.40.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.40 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.40)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.40.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.40 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.40)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.40 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.40)
ARMSE.error.infer.clust.cov.100.women.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.40.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.40 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.40)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.40.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.40 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.40)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.40 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.40)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.40.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.40 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.40)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.40.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.40 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.40)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.40 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.40)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.40.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.40 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.40)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.40.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.40 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.40)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.40 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.40)
ARMSE.error.infer.clust.cov.100.women.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.40.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.40 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.40)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.40 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.40.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.40 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.40)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.40 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.40)
ARMSE.error.infer.clust.cov.100.women.cov.40 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.40.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.40 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.40)


# Cov 45


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.45.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.45 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.45)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.45)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.45.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.45 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.45)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.45.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.45 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.45)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.45 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.45)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.45.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.45 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.45)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.45.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.45 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.45)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.45)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.45.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.45 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.45)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.45.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.45 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.45)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.45)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.45.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.45 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.45)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.45.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.45 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.45)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.45 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.45)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.45.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.45 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.45)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.45.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.45 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.45)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.45 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.45)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.45.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.45 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.45)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.45.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.45 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.45)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.45 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.45)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.45.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.45 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.45)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.45.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.45 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.45)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.45 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.45)
ARMSE.error.infer.clust.cov.100.women.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.45.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.45 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.45)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.45.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.45 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.45)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.45 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.45)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.45.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.45 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.45)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.45.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.45 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.45)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.45 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.45)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.45.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.45 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.45)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.45.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.45 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.45)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.45 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.45)
ARMSE.error.infer.clust.cov.100.women.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.45.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.45 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.45)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.45.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.45 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.45)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.45 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.45)
ARMSE.error.infer.clust.cov.100.women.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.45.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.45 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.45)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.45.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.45 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.45)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.45 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.45)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.45.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.45 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.45)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.45.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.45 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.45)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.45 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.45)
ARMSE.error.infer.clust.cov.100.women.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.45.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.45 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.45)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.45.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.45 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.45)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.45 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.45)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.45.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.45 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.45)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.45.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.45 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.45)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.45 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.45)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.45.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.45 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.45)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.45.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.45 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.45)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.45 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.45)
ARMSE.error.infer.clust.cov.100.women.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.45.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.45 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.45)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.45 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.45.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.45 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.45)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.45 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.45)
ARMSE.error.infer.clust.cov.100.women.cov.45 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.45.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.45 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.45)


# Cov 50


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.50.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.50 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.50)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.50)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.50.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.50 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.50)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.50.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.50 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.50)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.50 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.50)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.50.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.50 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.50)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.50.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.50 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.50)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.50)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.50.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.50 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.50)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.50.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.50 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.50)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.50)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.50.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.50 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.50)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.50.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.50 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.50)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.50 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.50)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.50.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.50 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.50)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.50.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.50 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.50)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.50 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.50)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.50.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.50 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.50)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.50.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.50 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.50)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.50 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.50)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.50.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.50 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.50)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.50.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.50 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.50)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.50 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.50)
ARMSE.error.infer.clust.cov.100.women.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.50.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.50 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.50)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.50.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.50 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.50)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.50 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.50)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.50.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.50 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.50)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.50.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.50 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.50)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.50 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.50)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.50.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.50 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.50)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.50.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.50 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.50)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.50 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.50)
ARMSE.error.infer.clust.cov.100.women.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.50.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.50 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.50)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.50.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.50 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.50)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.50 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.50)
ARMSE.error.infer.clust.cov.100.women.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.50.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.50 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.50)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.50.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.50 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.50)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.50 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.50)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.50.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.50 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.50)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.50.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.50 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.50)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.50 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.50)
ARMSE.error.infer.clust.cov.100.women.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.50.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.50 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.50)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.50.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.50 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.50)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.50 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.50)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.50.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.50 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.50)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.50.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.50 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.50)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.50 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.50)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.50.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.50 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.50)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.50.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.50 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.50)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.50 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.50)
ARMSE.error.infer.clust.cov.100.women.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.50.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.50 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.50)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.50 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.50.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.50 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.50)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.50 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.50)
ARMSE.error.infer.clust.cov.100.women.cov.50 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.50.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.50 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.50)


# Cov 55


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.55.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.55 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.55)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.55)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.55.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.55 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.55)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.55.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.55 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.55)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.55 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.55)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.55.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.55 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.55)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.55.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.55 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.55)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.55)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.55.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.55 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.55)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.55.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.55 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.55)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.55)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.55.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.55 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.55)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.55.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.55 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.55)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.55 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.55)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.55.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.55 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.55)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.55.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.55 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.55)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.55 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.55)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.55.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.55 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.55)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.55.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.55 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.55)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.55 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.55)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.55.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.55 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.55)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.55.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.55 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.55)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.55 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.55)
ARMSE.error.infer.clust.cov.100.women.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.55.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.55 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.55)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.55.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.55 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.55)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.55 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.55)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.55.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.55 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.55)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.55.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.55 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.55)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.55 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.55)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.55.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.55 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.55)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.55.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.55 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.55)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.55 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.55)
ARMSE.error.infer.clust.cov.100.women.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.55.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.55 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.55)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.55.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.55 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.55)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.55 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.55)
ARMSE.error.infer.clust.cov.100.women.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.55.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.55 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.55)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.55.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.55 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.55)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.55 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.55)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.55.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.55 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.55)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.55.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.55 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.55)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.55 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.55)
ARMSE.error.infer.clust.cov.100.women.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.55.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.55 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.55)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.55.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.55 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.55)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.55 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.55)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.55.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.55 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.55)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.55.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.55 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.55)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.55 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.55)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.55.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.55 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.55)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.55.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.55 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.55)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.55 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.55)
ARMSE.error.infer.clust.cov.100.women.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.55.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.55 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.55)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.55 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.55.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.55 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.55)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.55 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.55)
ARMSE.error.infer.clust.cov.100.women.cov.55 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.55.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.55 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.55)



# Cov 60


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.60.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.60 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.60)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.60)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.60.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.60 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.60)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.60.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.60 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.60)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.60 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.60)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.60.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.60 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.60)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.60.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.60 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.60)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.60)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.60.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.60 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.60)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.60.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.60 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.60)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.60)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.60.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.60 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.60)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.60.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.60 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.60)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.60 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.60)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.60.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.60 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.60)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.60.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.60 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.60)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.60 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.60)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.60.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.60 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.60)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.60.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.60 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.60)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.60 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.60)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.60.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.60 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.60)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.60.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.60 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.60)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.60 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.60)
ARMSE.error.infer.clust.cov.100.women.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.60.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.60 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.60)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.60.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.60 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.60)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.60 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.60)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.60.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.60 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.60)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.60.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.60 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.60)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.60 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.60)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.60.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.60 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.60)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.60.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.60 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.60)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.60 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.60)
ARMSE.error.infer.clust.cov.100.women.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.60.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.60 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.60)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.60.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.60 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.60)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.60 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.60)
ARMSE.error.infer.clust.cov.100.women.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.60.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.60 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.60)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.60.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.60 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.60)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.60 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.60)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.60.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.60 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.60)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.60.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.60 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.60)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.60 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.60)
ARMSE.error.infer.clust.cov.100.women.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.60.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.60 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.60)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.60.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.60 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.60)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.60 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.60)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.60.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.60 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.60)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.60.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.60 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.60)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.60 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.60)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.60.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.60 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.60)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.60.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.60 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.60)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.60 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.60)
ARMSE.error.infer.clust.cov.100.women.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.60.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.60 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.60)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.60 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.60.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.60 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.60)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.60 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.60)
ARMSE.error.infer.clust.cov.100.women.cov.60 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.60.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.60 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.60)


# Cov 65


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.65.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.65 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.65)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.65)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.65.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.65 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.65)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.65.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.65 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.65)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.65 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.65)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.65.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.65 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.65)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.65.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.65 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.65)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.65)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.65.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.65 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.65)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.65.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.65 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.65)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.65)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.65.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.65 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.65)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.65.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.65 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.65)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.65 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.65)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.65.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.65 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.65)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.65.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.65 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.65)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.65 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.65)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.65.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.65 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.65)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.65.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.65 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.65)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.65 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.65)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.65.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.65 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.65)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.65.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.65 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.65)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.65 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.65)
ARMSE.error.infer.clust.cov.100.women.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.65.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.65 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.65)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.65.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.65 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.65)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.65 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.65)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.65.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.65 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.65)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.65.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.65 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.65)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.65 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.65)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.65.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.65 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.65)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.65.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.65 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.65)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.65 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.65)
ARMSE.error.infer.clust.cov.100.women.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.65.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.65 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.65)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.65.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.65 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.65)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.65 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.65)
ARMSE.error.infer.clust.cov.100.women.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.65.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.65 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.65)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.65.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.65 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.65)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.65 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.65)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.65.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.65 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.65)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.65.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.65 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.65)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.65 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.65)
ARMSE.error.infer.clust.cov.100.women.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.65.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.65 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.65)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.65.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.65 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.65)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.65 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.65)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.65.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.65 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.65)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.65.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.65 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.65)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.65 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.65)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.65.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.65 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.65)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.65.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.65 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.65)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.65 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.65)
ARMSE.error.infer.clust.cov.100.women.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.65.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.65 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.65)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.65 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.65.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.65 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.65)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.65 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.65)
ARMSE.error.infer.clust.cov.100.women.cov.65 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.65.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.65 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.65)


# Cov 70


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.70.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.70 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.70)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.70)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.70.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.70 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.70)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.70.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.70 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.70)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.70 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.70)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.70.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.70 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.70)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.70.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.70 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.70)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.70)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.70.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.70 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.70)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.70.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.70 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.70)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.70)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.70.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.70 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.70)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.70.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.70 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.70)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.70 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.70)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.70.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.70 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.70)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.70.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.70 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.70)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.70 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.70)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.70.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.70 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.70)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.70.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.70 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.70)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.70 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.70)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.70.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.70 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.70)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.70.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.70 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.70)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.70 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.70)
ARMSE.error.infer.clust.cov.100.women.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.70.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.70 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.70)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.70.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.70 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.70)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.70 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.70)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.70.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.70 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.70)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.70.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.70 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.70)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.70 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.70)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.70.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.70 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.70)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.70.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.70 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.70)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.70 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.70)
ARMSE.error.infer.clust.cov.100.women.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.70.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.70 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.70)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.70.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.70 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.70)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.70 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.70)
ARMSE.error.infer.clust.cov.100.women.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.70.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.70 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.70)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.70.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.70 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.70)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.70 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.70)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.70.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.70 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.70)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.70.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.70 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.70)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.70 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.70)
ARMSE.error.infer.clust.cov.100.women.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.70.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.70 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.70)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.70.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.70 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.70)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.70 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.70)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.70.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.70 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.70)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.70.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.70 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.70)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.70 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.70)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.70.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.70 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.70)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.70.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.70 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.70)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.70 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.70)
ARMSE.error.infer.clust.cov.100.women.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.70.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.70 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.70)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.70 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.70.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.70 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.70)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.70 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.70)
ARMSE.error.infer.clust.cov.100.women.cov.70 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.70.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.70 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.70)


# Cov 75


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.75.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.75 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.75)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.75)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.75.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.75 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.75)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.75.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.75 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.75)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.75 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.75)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.75.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.75 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.75)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.75.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.75 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.75)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.75)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.75.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.75 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.75)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.75.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.75 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.75)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.75)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.75.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.75 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.75)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.75.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.75 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.75)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.75 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.75)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.75.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.75 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.75)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.75.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.75 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.75)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.75 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.75)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.75.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.75 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.75)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.75.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.75 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.75)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.75 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.75)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.75.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.75 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.75)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.75.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.75 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.75)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.75 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.75)
ARMSE.error.infer.clust.cov.100.women.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.75.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.75 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.75)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.75.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.75 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.75)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.75 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.75)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.75.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.75 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.75)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.75.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.75 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.75)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.75 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.75)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.75.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.75 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.75)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.75.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.75 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.75)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.75 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.75)
ARMSE.error.infer.clust.cov.100.women.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.75.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.75 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.75)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.75.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.75 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.75)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.75 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.75)
ARMSE.error.infer.clust.cov.100.women.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.75.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.75 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.75)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.75.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.75 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.75)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.75 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.75)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.75.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.75 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.75)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.75.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.75 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.75)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.75 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.75)
ARMSE.error.infer.clust.cov.100.women.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.75.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.75 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.75)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.75.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.75 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.75)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.75 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.75)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.75.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.75 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.75)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.75.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.75 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.75)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.75 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.75)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.75.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.75 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.75)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.75.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.75 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.75)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.75 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.75)
ARMSE.error.infer.clust.cov.100.women.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.75.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.75 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.75)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.75 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.75.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.75 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.75)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.75 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.75)
ARMSE.error.infer.clust.cov.100.women.cov.75 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.75.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.75 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.75)


# Cov 80


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.80.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.80 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.80)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.80)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.80.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.80 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.80)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.80.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.80 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.80)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.80 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.80)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.80.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.80 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.80)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.80.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.80 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.80)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.80)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.80.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.80 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.80)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.80.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.80 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.80)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.80)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.80.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.80 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.80)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.80.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.80 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.80)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.80 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.80)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.80.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.80 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.80)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.80.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.80 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.80)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.80 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.80)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.80.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.80 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.80)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.80.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.80 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.80)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.80 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.80)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.80.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.80 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.80)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.80.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.80 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.80)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.80 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.80)
ARMSE.error.infer.clust.cov.100.women.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.80.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.80 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.80)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.80.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.80 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.80)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.80 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.80)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.80.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.80 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.80)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.80.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.80 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.80)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.80 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.80)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.80.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.80 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.80)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.80.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.80 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.80)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.80 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.80)
ARMSE.error.infer.clust.cov.100.women.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.80.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.80 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.80)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.80.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.80 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.80)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.80 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.80)
ARMSE.error.infer.clust.cov.100.women.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.80.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.80 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.80)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.80.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.80 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.80)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.80 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.80)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.80.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.80 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.80)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.80.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.80 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.80)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.80 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.80)
ARMSE.error.infer.clust.cov.100.women.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.80.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.80 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.80)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.80.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.80 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.80)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.80 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.80)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.80.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.80 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.80)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.80.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.80 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.80)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.80 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.80)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.80.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.80 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.80)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.80.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.80 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.80)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.80 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.80)
ARMSE.error.infer.clust.cov.100.women.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.80.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.80 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.80)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.80 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.80.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.80 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.80)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.80 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.80)
ARMSE.error.infer.clust.cov.100.women.cov.80 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.80.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.80 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.80)


# Cov 85


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.85.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.85 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.85)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.85)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.85.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.85 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.85)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.85.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.85 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.85)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.85 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.85)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.85.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.85 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.85)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.85.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.85 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.85)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.85)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.85.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.85 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.85)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.85.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.85 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.85)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.85)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.85.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.85 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.85)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.85.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.85 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.85)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.85 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.85)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.85.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.85 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.85)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.85.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.85 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.85)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.85 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.85)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.85.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.85 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.85)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.85.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.85 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.85)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.85 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.85)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.85.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.85 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.85)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.85.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.85 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.85)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.85 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.85)
ARMSE.error.infer.clust.cov.100.women.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.85.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.85 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.85)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.85.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.85 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.85)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.85 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.85)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.85.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.85 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.85)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.85.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.85 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.85)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.85 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.85)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.85.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.85 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.85)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.85.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.85 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.85)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.85 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.85)
ARMSE.error.infer.clust.cov.100.women.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.85.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.85 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.85)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.85.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.85 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.85)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.85 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.85)
ARMSE.error.infer.clust.cov.100.women.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.85.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.85 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.85)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.85.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.85 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.85)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.85 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.85)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.85.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.85 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.85)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.85.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.85 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.85)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.85 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.85)
ARMSE.error.infer.clust.cov.100.women.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.85.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.85 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.85)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.85.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.85 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.85)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.85 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.85)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.85.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.85 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.85)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.85.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.85 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.85)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.85 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.85)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.85.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.85 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.85)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.85.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.85 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.85)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.85 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.85)
ARMSE.error.infer.clust.cov.100.women.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.85.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.85 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.85)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.85 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.85.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.85 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.85)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.85 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.85)
ARMSE.error.infer.clust.cov.100.women.cov.85 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.85.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.85 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.85)


# Cov 90


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.90.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.90 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.90)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.90)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.90.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.90 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.90)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.90.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.90 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.90)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.90 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.90)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.90.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.90 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.90)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.90.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.90 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.90)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.90)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.90.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.90 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.90)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.90.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.90 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.90)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.90)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.90.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.90 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.90)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.90.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.90 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.90)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.90 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.90)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.90.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.90 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.90)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.90.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.90 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.90)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.90 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.90)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.90.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.90 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.90)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.90.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.90 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.90)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.90 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.90)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.90.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.90 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.90)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.90.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.90 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.90)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.90 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.90)
ARMSE.error.infer.clust.cov.100.women.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.90.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.90 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.90)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.90.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.90 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.90)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.90 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.90)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.90.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.90 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.90)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.90.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.90 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.90)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.90 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.90)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.90.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.90 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.90)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.90.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.90 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.90)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.90 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.90)
ARMSE.error.infer.clust.cov.100.women.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.90.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.90 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.90)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.90.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.90 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.90)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.90 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.90)
ARMSE.error.infer.clust.cov.100.women.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.90.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.90 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.90)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.90.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.90 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.90)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.90 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.90)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.90.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.90 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.90)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.90.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.90 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.90)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.90 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.90)
ARMSE.error.infer.clust.cov.100.women.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.90.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.90 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.90)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.90.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.90 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.90)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.90 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.90)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.90.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.90 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.90)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.90.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.90 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.90)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.90 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.90)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.90.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.90 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.90)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.90.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.90 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.90)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.90 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.90)
ARMSE.error.infer.clust.cov.100.women.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.90.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.90 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.90)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.90 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.90.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.90 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.90)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.90 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.90)
ARMSE.error.infer.clust.cov.100.women.cov.90 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.90.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.90 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.90)



# Cov 95


# 15.25

# M.15.25.F.15.25
error.infer.clust.cov.100.men.15.25.F.15.25.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.15.25) - as.numeric(vector.MAR.cov.95.cl.prop.men15.25.F.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.95 <- RMSE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.95)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.95)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.15.25, v2=vector.MAR.cov.95.cl.prop.men15.25.F.15.25)
MRE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.95 <- MRE(error.infer.clust.cov.100.men.15.25.F.15.25.cov.95)
# F.15.25.M.15.25
error.infer.clust.cov.100.women.15.25.M.15.25.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.15.25) - as.numeric(vector.MAR.cov.95.cl.prop.women15.25.M.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.95 <- RMSE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.95)
MAE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.95 <- MAE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.95)
ARMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.15.25, v2=vector.MAR.cov.95.cl.prop.women15.25.M.15.25)
MRE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.95 <- MRE(error.infer.clust.cov.100.women.15.25.M.15.25.cov.95)


# M.15.25.F.25.40
error.infer.clust.cov.100.men.15.25.F.25.40.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.25.40) - as.numeric(vector.MAR.cov.95.cl.prop.men15.25.F.25.40)
RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.95 <- RMSE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.95)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.95)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.25.40, v2=vector.MAR.cov.95.cl.prop.men15.25.F.25.40)
MRE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.95 <- MRE(error.infer.clust.cov.100.men.15.25.F.25.40.cov.95)
# M.15.25.F.40.50
error.infer.clust.cov.100.men.15.25.F.40.50.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.men15.25.F.40.50) - as.numeric(vector.MAR.cov.95.cl.prop.men15.25.F.40.50)
RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.95 <- RMSE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.95)
MAE.error.infer.clust.cov.100.men.15.25cov.35 <- MAE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.95)
ARMSE.error.infer.clust.cov.100.men.15.25cov.35 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men15.25.F.40.50, v2=vector.MAR.cov.95.cl.prop.men15.25.F.40.50)
MRE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.95 <- MRE(error.infer.clust.cov.100.men.15.25.F.40.50.cov.95)


# F.15.25.M.25.40
error.infer.clust.cov.100.women.15.25.M.25.40.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.25.40) - as.numeric(vector.MAR.cov.95.cl.prop.women15.25.M.25.40)
RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.95 <- RMSE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.95)
MAE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.95 <- MAE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.95)
ARMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.25.40, v2=vector.MAR.cov.95.cl.prop.women15.25.M.25.40)
MRE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.95 <- MRE(error.infer.clust.cov.100.women.15.25.M.25.40.cov.95)
# F.15.25.M.40.50
error.infer.clust.cov.100.women.15.25.M.40.50.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.women15.25.M.40.50) - as.numeric(vector.MAR.cov.95.cl.prop.women15.25.M.40.50)
RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.95 <- RMSE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.95)
MAE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.95 <- MAE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.95)
ARMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women15.25.M.40.50, v2=vector.MAR.cov.95.cl.prop.women15.25.M.40.50)
MRE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.95 <- MRE(error.infer.clust.cov.100.women.15.25.M.40.50.cov.95)



# 25.40

# M.25.40.F.25.40
error.infer.clust.cov.100.men.25.40.F.25.40.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.25.40) - as.numeric(vector.MAR.cov.95.cl.prop.men25.40.F.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.95 <- RMSE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.95)
MAE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.95 <- MAE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.95)
ARMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.25.40, v2=vector.MAR.cov.95.cl.prop.men25.40.F.25.40)
MRE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.95 <- MRE(error.infer.clust.cov.100.men.25.40.F.25.40.cov.95)
# F.25.40.M.25.40
error.infer.clust.cov.100.women.25.40.M.25.40.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.25.40) - as.numeric(vector.MAR.cov.95.cl.prop.women25.40.M.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.95 <- RMSE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.95)
MAE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.95 <- MAE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.95)
ARMSE.error.infer.clust.cov.100.women.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.25.40, v2=vector.MAR.cov.95.cl.prop.women25.40.M.25.40)
MRE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.95 <- MRE(error.infer.clust.cov.100.women.25.40.M.25.40.cov.95)


# M.25.40.F.15.25
error.infer.clust.cov.100.men.25.40.F.15.25.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.15.25) - as.numeric(vector.MAR.cov.95.cl.prop.men25.40.F.15.25)
RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.95 <- RMSE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.95)
MAE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.95 <- MAE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.95)
ARMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.15.25, v2=vector.MAR.cov.95.cl.prop.men25.40.F.15.25)
MRE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.95 <- MRE(error.infer.clust.cov.100.men.25.40.F.15.25.cov.95)
# M.25.40.F.40.50
error.infer.clust.cov.100.men.25.40.F.40.50.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.men25.40.F.40.50) - as.numeric(vector.MAR.cov.95.cl.prop.men25.40.F.40.50)
RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.95 <- RMSE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.95)
MAE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.95 <- MAE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.95)
ARMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men25.40.F.40.50, v2=vector.MAR.cov.95.cl.prop.men25.40.F.40.50)
MRE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.95 <- MRE(error.infer.clust.cov.100.men.25.40.F.40.50.cov.95)


# F.25.40.M.15.25
error.infer.clust.cov.100.women.25.40.M.15.25.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.15.25) - as.numeric(vector.MAR.cov.95.cl.prop.women25.40.M.15.25)
RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.95 <- RMSE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.95)
MAE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.95 <- MAE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.95)
ARMSE.error.infer.clust.cov.100.women.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.15.25, v2=vector.MAR.cov.95.cl.prop.women25.40.M.15.25)
MRE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.95 <- MRE(error.infer.clust.cov.100.women.25.40.M.15.25.cov.95)
# F.25.40.M.40.50
error.infer.clust.cov.100.women.25.40.M.40.50.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.women25.40.M.40.50) - as.numeric(vector.MAR.cov.95.cl.prop.women25.40.M.40.50)
RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.95 <- RMSE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.95)
MAE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.95 <- MAE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.95)
ARMSE.error.infer.clust.cov.100.women.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women25.40.M.40.50, v2=vector.MAR.cov.95.cl.prop.women25.40.M.40.50)
MRE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.95 <- MRE(error.infer.clust.cov.100.women.25.40.M.40.50.cov.95)



# 40.50

# M.40.50.F.40.50
error.infer.clust.cov.100.men.40.50.F.40.50.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.40.50) - as.numeric(vector.MAR.cov.95.cl.prop.men40.50.F.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.95 <- RMSE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.95)
MAE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.95 <- MAE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.95)
ARMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.40.50, v2=vector.MAR.cov.95.cl.prop.men40.50.F.40.50)
MRE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.95 <- MRE(error.infer.clust.cov.100.men.40.50.F.40.50.cov.95)
# F.40.50.M.40.50
error.infer.clust.cov.100.women.40.50.M.40.50.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.40.50) - as.numeric(vector.MAR.cov.95.cl.prop.women40.50.M.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.95 <- RMSE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.95)
MAE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.95 <- MAE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.95)
ARMSE.error.infer.clust.cov.100.women.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.40.50, v2=vector.MAR.cov.95.cl.prop.women40.50.M.40.50)
MRE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.95 <- MRE(error.infer.clust.cov.100.women.40.50.M.40.50.cov.95)


# M.40.50.F.15.25
error.infer.clust.cov.100.men.40.50.F.15.25.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.15.25) - as.numeric(vector.MAR.cov.95.cl.prop.men40.50.F.15.25)
RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.95 <- RMSE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.95)
MAE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.95 <- MAE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.95)
ARMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.15.25, v2=vector.MAR.cov.95.cl.prop.men40.50.F.15.25)
MRE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.95 <- MRE(error.infer.clust.cov.100.men.40.50.F.15.25.cov.95)
# M.40.50.F.25.40
error.infer.clust.cov.100.men.40.50.F.25.40.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.men40.50.F.25.40) - as.numeric(vector.MAR.cov.95.cl.prop.men40.50.F.25.40)
RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.95 <- RMSE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.95)
MAE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.95 <- MAE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.95)
ARMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.men40.50.F.25.40, v2=vector.MAR.cov.95.cl.prop.men40.50.F.25.40)
MRE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.95 <- MRE(error.infer.clust.cov.100.men.40.50.F.25.40.cov.95)


# F.40.50.M.15.25
error.infer.clust.cov.100.women.40.50.M.15.25.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.15.25) - as.numeric(vector.MAR.cov.95.cl.prop.women40.50.M.15.25)
RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.95 <- RMSE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.95)
MAE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.95 <- MAE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.95)
ARMSE.error.infer.clust.cov.100.women.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.15.25, v2=vector.MAR.cov.95.cl.prop.women40.50.M.15.25)
MRE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.95 <- MRE(error.infer.clust.cov.100.women.40.50.M.15.25.cov.95)
# F.40.50.M.25.40
error.infer.clust.cov.100.women.40.50.M.25.40.cov.95 <- as.numeric(vector.MAR.true.cov.100.prop.women40.50.M.25.40) - as.numeric(vector.MAR.cov.95.cl.prop.women40.50.M.25.40)
RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.95 <- RMSE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.95)
MAE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.95 <- MAE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.95)
ARMSE.error.infer.clust.cov.100.women.cov.95 <- ARMSE(v1=vector.MAR.true.cov.100.prop.women40.50.M.25.40, v2=vector.MAR.cov.95.cl.prop.women40.50.M.25.40)
MRE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.95 <- MRE(error.infer.clust.cov.100.women.40.50.M.25.40.cov.95)
# Figures -----------------


RMSE.error.infer.clust.cov.100.prop.men.15.25.F.15.25 <- data.frame(x=c("35", "40", "45", "50", "55", "60", 
                                                                        "65", "70", "75", "80", "85", "90",  "95"),
                                                                    
                                                                    F = c(RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.35, RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.40,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.45, RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.50,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.55, RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.60,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.65, RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.70, 
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.75, RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.80,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.85, RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.90,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.15.25.cov.95))

RMSE.error.infer.clust.cov.100.prop.men.15.25.F.15.25$param <- rep("M.15.25.F.15.25", nrow(RMSE.error.infer.clust.cov.100.prop.men.15.25.F.15.25))

plot.RMSE.error.infer.clust.cov.100.prop.men.15.25.F.15.25 <- ggplot(RMSE.error.infer.clust.cov.100.prop.men.15.25.F.15.25, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for men 15 - 25 with women 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.men.15.25.F.15.25
RMSE.error.infer.clust.cov.100.prop.men.15.25.F.25.40 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                    
                                                                    F = c(RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.35, RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.40,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.45, RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.50,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.55, RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.60,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.65, RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.70, 
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.75, RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.80,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.85, RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.90,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.25.40.cov.95))


RMSE.error.infer.clust.cov.100.prop.men.15.25.F.25.40$param <- rep("M.15.25.F.25.40", nrow(RMSE.error.infer.clust.cov.100.prop.men.15.25.F.25.40))

plot.RMSE.error.infer.clust.cov.100.prop.men.15.25.F.25.40 <- ggplot(RMSE.error.infer.clust.cov.100.prop.men.15.25.F.25.40, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for men 15 - 25 and women 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.men.15.25.F.25.40
RMSE.error.infer.clust.cov.100.prop.men.15.25.F.40.50 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                    
                                                                    F = c(RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.35, RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.40,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.45, RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.50,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.55, RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.60,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.65, RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.70, 
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.75, RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.80,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.85, RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.90,
                                                                          RMSE.error.infer.clust.cov.100.men.15.25.F.40.50.cov.95))


RMSE.error.infer.clust.cov.100.prop.men.15.25.F.40.50$param <- rep("M.15.25.F.40.50", nrow(RMSE.error.infer.clust.cov.100.prop.men.15.25.F.40.50))


plot.RMSE.error.infer.clust.cov.100.prop.men.15.25.F.40.50 <- ggplot(RMSE.error.infer.clust.cov.100.prop.men.15.25.F.40.50, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for men 15 - 25 with women 40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.men.15.25.F.40.50
RMSE.error.infer.clust.cov.100.prop.women.15.25.M.15.25 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                      
                                                                      F = c(RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.35, RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.40,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.45, RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.50,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.55, RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.60,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.65, RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.70, 
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.75, RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.80,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.85, RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.90,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.15.25.cov.95))



RMSE.error.infer.clust.cov.100.prop.women.15.25.M.15.25$param <- rep("F.15.25.M.15.25", nrow(RMSE.error.infer.clust.cov.100.prop.women.15.25.M.15.25))


plot.RMSE.error.infer.clust.cov.100.prop.women.15.25.M.15.25 <- ggplot(RMSE.error.infer.clust.cov.100.prop.women.15.25.M.15.25, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for women 15 - 25 with men  in 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.women.15.25.M.15.25
RMSE.error.infer.clust.cov.100.prop.women.15.25.M.25.40 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                      
                                                                      F = c(RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.35, RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.40,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.45, RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.50,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.55, RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.60,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.65, RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.70, 
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.75, RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.80,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.85, RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.90,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.25.40.cov.95))


RMSE.error.infer.clust.cov.100.prop.women.15.25.M.25.40$param <- rep("F.15.25.M.25.40", nrow(RMSE.error.infer.clust.cov.100.prop.women.15.25.M.25.40))


plot.RMSE.error.infer.clust.cov.100.prop.women.15.25.M.25.40 <- ggplot(RMSE.error.infer.clust.cov.100.prop.women.15.25.M.25.40, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for women 15 - 25 with men  25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.women.15.25.M.25.40
RMSE.error.infer.clust.cov.100.prop.women.15.25.M.40.50 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                      
                                                                      F = c(RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.35, RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.40,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.45, RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.50,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.55, RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.60,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.65, RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.70, 
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.75, RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.80,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.85, RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.90,
                                                                            RMSE.error.infer.clust.cov.100.women.15.25.M.40.50.cov.95))


RMSE.error.infer.clust.cov.100.prop.women.15.25.M.40.50$param <- rep("F.15.25.M.40.50", nrow(RMSE.error.infer.clust.cov.100.prop.women.15.25.M.40.50))


plot.RMSE.error.infer.clust.cov.100.prop.women.15.25.M.40.50 <- ggplot(RMSE.error.infer.clust.cov.100.prop.women.15.25.M.40.50, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for women 15 - 25 with men  40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.women.15.25.M.40.50
RMSE.error.infer.clust.cov.100.prop.men.25.40.F.25.40 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                    
                                                                    F = c(RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.35, RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.40,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.45, RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.50,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.55, RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.60,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.65, RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.70, 
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.75, RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.80,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.85, RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.90,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.25.40.cov.95))


RMSE.error.infer.clust.cov.100.prop.men.25.40.F.25.40$param <- rep("M.25.40.F.25.40", nrow(RMSE.error.infer.clust.cov.100.prop.men.25.40.F.25.40))

plot.RMSE.error.infer.clust.cov.100.prop.men.25.40.F.25.40 <- ggplot(RMSE.error.infer.clust.cov.100.prop.men.25.40.F.25.40, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for men 25 - 40 with women in 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.men.25.40.F.25.40
RMSE.error.infer.clust.cov.100.prop.men.25.40.F.15.25 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                    
                                                                    F = c(RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.35, RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.40,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.45, RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.50,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.55, RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.60,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.65, RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.70, 
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.75, RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.80,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.85, RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.90,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.15.25.cov.95))


RMSE.error.infer.clust.cov.100.prop.men.25.40.F.15.25$param <- rep("M.25.40.F.15.25", nrow(RMSE.error.infer.clust.cov.100.prop.men.25.40.F.15.25))


plot.RMSE.error.infer.clust.cov.100.prop.men.25.40.F.15.25 <- ggplot(RMSE.error.infer.clust.cov.100.prop.men.25.40.F.15.25, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for men 25 - 40 with women 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.men.25.40.F.15.25
RMSE.error.infer.clust.cov.100.prop.men.25.40.F.40.50 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                    
                                                                    F = c(RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.35, RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.40,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.45, RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.50,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.55, RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.60,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.65, RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.70, 
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.75, RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.80,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.85, RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.90,
                                                                          RMSE.error.infer.clust.cov.100.men.25.40.F.40.50.cov.95))



RMSE.error.infer.clust.cov.100.prop.men.25.40.F.40.50$param <- rep("M.25.40.F.40.50", nrow(RMSE.error.infer.clust.cov.100.prop.men.25.40.F.40.50))


plot.RMSE.error.infer.clust.cov.100.prop.men.25.40.F.40.50 <- ggplot(RMSE.error.infer.clust.cov.100.prop.men.25.40.F.40.50, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for men 25 - 40 with women 40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.men.25.40.F.40.50
RMSE.error.infer.clust.cov.100.prop.women.25.40.M.25.40 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                      
                                                                      F = c(RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.35, RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.40,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.45, RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.50,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.55, RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.60,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.65, RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.70, 
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.75, RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.80,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.85, RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.90,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.25.40.cov.95))



RMSE.error.infer.clust.cov.100.prop.women.25.40.M.25.40$param <- rep("F.25.40.M.25.40", nrow(RMSE.error.infer.clust.cov.100.prop.women.25.40.M.25.40))

plot.RMSE.error.infer.clust.cov.100.prop.women.25.40.M.25.40 <- ggplot(RMSE.error.infer.clust.cov.100.prop.women.25.40.M.25.40, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for women 25 - 40 with men in 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.prop.women.25.40.M.25.40
RMSE.error.infer.clust.cov.100.prop.women.25.40.M.15.25 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                      
                                                                      F = c(RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.35, RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.40,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.45, RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.50,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.55, RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.60,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.65, RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.70, 
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.75, RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.80,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.85, RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.90,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.15.25.cov.95))



RMSE.error.infer.clust.cov.100.prop.women.25.40.M.15.25$param <- rep("F.25.40.M.15.25", nrow(RMSE.error.infer.clust.cov.100.prop.women.25.40.M.15.25))


plot.RMSE.error.infer.clust.cov.100.prop.women.25.40.M.15.25 <- ggplot(RMSE.error.infer.clust.cov.100.prop.women.25.40.M.15.25, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for women 25 - 40 with men 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.prop.women.25.40.M.15.25
RMSE.error.infer.clust.cov.100.prop.women.25.40.M.40.50 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                      
                                                                      F = c(RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.35, RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.40,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.45, RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.50,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.55, RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.60,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.65, RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.70, 
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.75, RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.80,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.85, RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.90,
                                                                            RMSE.error.infer.clust.cov.100.women.25.40.M.40.50.cov.95))



RMSE.error.infer.clust.cov.100.prop.women.25.40.M.40.50$param <- rep("F.25.40.M.40.50", nrow(RMSE.error.infer.clust.cov.100.prop.women.25.40.M.40.50))


plot.RMSE.error.infer.clust.cov.100.prop.women.25.40.M.40.50 <- ggplot(RMSE.error.infer.clust.cov.100.prop.women.25.40.M.40.50, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for women 25 - 40 with men 40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.prop.women.25.40.M.40.50
RMSE.error.infer.clust.cov.100.prop.men.40.50.F.40.50 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                    
                                                                    F = c(RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.35, RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.40,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.45, RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.50,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.55, RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.60,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.65, RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.70, 
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.75, RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.80,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.85, RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.90,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.40.50.cov.95))



RMSE.error.infer.clust.cov.100.prop.men.40.50.F.40.50$param <- rep("M.40.50.F.40.50", nrow(RMSE.error.infer.clust.cov.100.prop.men.40.50.F.40.50))


plot.RMSE.error.infer.clust.cov.100.prop.men.40.50.F.40.50 <- ggplot(RMSE.error.infer.clust.cov.100.prop.men.40.50.F.40.50, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for men 40 - 50 with women in  40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.prop.men.40.50.F.40.50
RMSE.error.infer.clust.cov.100.prop.men.40.50.F.15.25 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                    
                                                                    F = c(RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.35, RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.40,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.45, RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.50,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.55, RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.60,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.65, RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.70, 
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.75, RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.80,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.85, RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.90,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.15.25.cov.95))


RMSE.error.infer.clust.cov.100.prop.men.40.50.F.15.25$param <- rep("M.40.50.F.15.25", nrow(RMSE.error.infer.clust.cov.100.prop.men.40.50.F.15.25))

plot.RMSE.error.infer.clust.cov.100.prop.men.40.50.F.15.25 <- ggplot(RMSE.error.infer.clust.cov.100.prop.men.40.50.F.15.25, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for men 40 - 50 with women 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.prop.men.40.50.F.15.25
RMSE.error.infer.clust.cov.100.prop.men.40.50.F.25.40 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                    
                                                                    F = c(RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.35, RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.40,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.45, RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.50,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.55, RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.60,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.65, RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.70, 
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.75, RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.80,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.85, RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.90,
                                                                          RMSE.error.infer.clust.cov.100.men.40.50.F.25.40.cov.95))


RMSE.error.infer.clust.cov.100.prop.men.40.50.F.25.40$param <- rep("M.40.50.F.25.40", nrow(RMSE.error.infer.clust.cov.100.prop.men.40.50.F.25.40))

plot.RMSE.error.infer.clust.cov.100.prop.men.40.50.F.25.40 <- ggplot(RMSE.error.infer.clust.cov.100.prop.men.40.50.F.25.40, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for men 40 - 50 with women 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.prop.men.40.50.F.25.40
RMSE.error.infer.clust.cov.100.prop.women.40.50.M.40.50 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                      
                                                                      F = c(RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.35, RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.40,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.45, RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.50,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.55, RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.60,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.65, RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.70, 
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.75, RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.80,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.85, RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.90,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.40.50.cov.95))


RMSE.error.infer.clust.cov.100.prop.women.40.50.M.40.50$param <- rep("F.40.50.M.40.50", nrow(RMSE.error.infer.clust.cov.100.prop.women.40.50.M.40.50))

plot.RMSE.error.infer.clust.cov.100.prop.women.40.50.M.40.50 <- ggplot(RMSE.error.infer.clust.cov.100.prop.women.40.50.M.40.50, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for women 40 - 50 with men  in 40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.women.40.50.M.40.50
RMSE.error.infer.clust.cov.100.prop.women.40.50.M.15.25 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                      
                                                                      F = c(RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.35, RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.40,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.45, RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.50,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.55, RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.60,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.65, RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.70, 
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.75, RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.80,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.85, RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.90,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.15.25.cov.95))


RMSE.error.infer.clust.cov.100.prop.women.40.50.M.15.25$param <- rep("F.40.50.M.15.25", nrow(RMSE.error.infer.clust.cov.100.prop.women.40.50.M.15.25))

plot.RMSE.error.infer.clust.cov.100.prop.women.40.50.M.15.25 <- ggplot(RMSE.error.infer.clust.cov.100.prop.women.40.50.M.15.25, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for women 40 - 50 with men 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.women.40.50.M.15.25
RMSE.error.infer.clust.cov.100.prop.women.40.50.M.25.40 <- data.frame(x=c("35", "40", "45",                                       "50", "55", "60",                                       "65", "70", "75",                                       "80", "85", "90",                                       "95"),
                                                                      
                                                                      F = c(RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.35, RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.40,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.45, RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.50,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.55, RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.60,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.65, RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.70, 
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.75, RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.80,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.85, RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.90,
                                                                            RMSE.error.infer.clust.cov.100.women.40.50.M.25.40.cov.95))


RMSE.error.infer.clust.cov.100.prop.women.40.50.M.25.40$param <- rep("F.40.50.M.25.40", nrow(RMSE.error.infer.clust.cov.100.prop.women.40.50.M.25.40))


plot.RMSE.error.infer.clust.cov.100.prop.women.40.50.M.25.40 <- ggplot(RMSE.error.infer.clust.cov.100.prop.women.40.50.M.25.40, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Proportion for women 40 - 50 with men 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.prop.women.40.50.M.25.40

4.3.1 Goodness of fit of proportions of pairings for critical age groups

df_gps <- rbind(
  # RMSE.error.infer.clust.cov.100.prop.women.40.50.M.25.40,
  # RMSE.error.infer.clust.cov.100.prop.women.40.50.M.15.25,
  # RMSE.error.infer.clust.cov.100.prop.women.40.50.M.40.50,
  RMSE.error.infer.clust.cov.100.prop.men.40.50.F.25.40,
  RMSE.error.infer.clust.cov.100.prop.men.40.50.F.15.25,
  # RMSE.error.infer.clust.cov.100.prop.men.40.50.F.40.50,
  RMSE.error.infer.clust.cov.100.prop.women.25.40.M.40.50,
  # RMSE.error.infer.clust.cov.100.prop.women.25.40.M.15.25,
  RMSE.error.infer.clust.cov.100.prop.women.25.40.M.25.40,
  # RMSE.error.infer.clust.cov.100.prop.men.25.40.F.40.50,
  RMSE.error.infer.clust.cov.100.prop.men.25.40.F.15.25,
  RMSE.error.infer.clust.cov.100.prop.men.25.40.F.25.40,
  RMSE.error.infer.clust.cov.100.prop.women.15.25.M.40.50,
  RMSE.error.infer.clust.cov.100.prop.women.15.25.M.25.40,
  RMSE.error.infer.clust.cov.100.prop.women.15.25.M.15.25,
  # RMSE.error.infer.clust.cov.100.prop.men.15.25.F.40.50,
  # RMSE.error.infer.clust.cov.100.prop.men.15.25.F.25.40,
  RMSE.error.infer.clust.cov.100.prop.men.15.25.F.15.25
)


d <- df_gps


newdata <- d[order(d$param),] 

newdata$f_m <- c(rep("Females_Males", nrow(newdata)/2), rep("Males_Females", nrow(newdata)/2))

newdata$age_groups <- c(rep("15_24 & 15_24", 13), rep("15_24 & 25_39", 13), rep("15_24 & 40_49", 13), rep("25_39 & 25_39", 13), rep("25_39 & 40_49", 13),
                        rep("15_24 & 15_24", 13), rep("15_24 & 25_39", 13), rep("25_39 & 25_39", 13), rep("15_24 & 40_49", 13), rep("25_39 & 40_49", 13))

colnames(newdata) <- c("cov", "val", "param", "f_m", "age_groups")


saveRDS(newdata, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/MAR_a_10_Error_for_Proportions_of_Pairings.RDS")


plot.prop_pairings_seq_cov_errors_100 <- ggplot(newdata, aes(x=cov, y=val, colour= age_groups, group = age_groups)) + 
  geom_line(size=1) +
  geom_point() +
  facet_grid(. ~ f_m) + 
  # theme(legend.position="top")+
  xlab("Sampling Coverage (%)") + ylab("Error value for proportion of pairings")

print(plot.prop_pairings_seq_cov_errors_100)

ggsave(filename = "Plot_a_10_Error_for_Proportions_of_Pairings.pdf",
       plot = plot.prop_pairings_seq_cov_errors_100,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 26, height = 15, units = "cm")


pdf("/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Plot_a_9_10_proportion_pairings_and_error_at_35_95_Coverage.pdf",
    width=15, height=15)
gridExtra::grid.arrange(plot.prop_pairings_seq_cov_target_v, plot.prop_pairings_seq_cov_errors_100)
dev.off()
## png 
##   2

5 Age difference in pairings from transmission clusters

From pairings, knowing the age of each individual, we can be able to compute the age difference between man and woman within same pair. This means we consider the age difference for each man/woman in a certain age group with same pair with a woman/man.

5.1 True age difference at 100% sampling (sequence) coverage

Within 35 - 40 simulation, hen we consider 100% sampling (sequence) coverage, the average of age difference between pairs are given in the following table:

AD.true.cov.100 <- dr.cov.100 %>%
  select(contains(".AD.")) 


## Vector

# Mean

vector.mean.AD.num.women.true.cov.100.15.25 <- AD.true.cov.100[,1] # AD.true.cov.100[,1]
vector.mean.AD.num.men.true.cov.100.15.25 <- AD.true.cov.100[,2] # AD.true.cov.100[,2]

vector.mean.AD.num.women.true.cov.100.25.40 <- AD.true.cov.100[,3] # AD.true.cov.100[,3]
vector.mean.AD.num.men.true.cov.100.25.40 <- AD.true.cov.100[,4] # AD.true.cov.100[,4]

vector.mean.AD.num.women.true.cov.100.40.50 <- AD.true.cov.100[,5] # AD.true.cov.100[,5]
vector.mean.AD.num.men.true.cov.100.40.50 <- AD.true.cov.100[,6] # AD.true.cov.100[,6]

# Median

vector.med.AD.num.women.true.cov.100.15.25 <- AD.true.cov.100[,7] # AD.true.cov.100[,7]
vector.med.AD.num.men.true.cov.100.15.25 <- AD.true.cov.100[,8] # AD.true.cov.100[,8]

vector.med.AD.num.women.true.cov.100.25.40 <- AD.true.cov.100[,9] # AD.true.cov.100[,9]
vector.med.AD.num.men.true.cov.100.25.40 <- AD.true.cov.100[,10] # AD.true.cov.100[,10]

vector.med.AD.num.women.true.cov.100.40.50 <- AD.true.cov.100[,11] # AD.true.cov.100[,11]
vector.med.AD.num.men.true.cov.100.40.50 <- AD.true.cov.100[,12] # AD.true.cov.100[,12]

# Standard deviation

vector.sd.AD.num.women.true.cov.100.15.25 <- AD.true.cov.100[,13] # AD.true.cov.100[,13]
vector.sd.AD.num.men.true.cov.100.15.25 <- AD.true.cov.100[,14] # AD.true.cov.100[,14]

vector.sd.AD.num.women.true.cov.100.25.40 <- AD.true.cov.100[,15] # AD.true.cov.100[,15]
vector.sd.AD.num.men.true.cov.100.25.40 <- AD.true.cov.100[,16] # AD.true.cov.100[,16]

vector.sd.AD.num.women.true.cov.100.40.50 <- AD.true.cov.100[,17] # AD.true.cov.100[,17]
vector.sd.AD.num.men.true.cov.100.40.50 <- AD.true.cov.100[,18] # AD.true.cov.100[,18]



# Summarised

# Mean

mean.AD.num.women.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,1])
mean.AD.num.men.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,2])

mean.AD.num.women.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,3])
mean.AD.num.men.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,4])

mean.AD.num.women.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,5])
mean.AD.num.men.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,6])

# Median

med.AD.num.women.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,7])
med.AD.num.men.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,8])

med.AD.num.women.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,9])
med.AD.num.men.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,10])

med.AD.num.women.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,11])
med.AD.num.men.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,12])

# Standard deviation

sd.AD.num.women.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,13])
sd.AD.num.men.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,14])

sd.AD.num.women.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,15])
sd.AD.num.men.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,16])

sd.AD.num.women.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,17])
sd.AD.num.men.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,18])



# Table of true AD statistics ta 100% coverage  --------


AD.100.val.F <- c(mean.AD.num.women.true.cov.100.15.25[2], mean.AD.num.men.true.cov.100.15.25[2],
                  mean.AD.num.women.true.cov.100.25.40[2], mean.AD.num.men.true.cov.100.25.40[2],
                  mean.AD.num.women.true.cov.100.40.50[2], mean.AD.num.men.true.cov.100.40.50[2],
                  
                  
                  med.AD.num.women.true.cov.100.15.25[2], med.AD.num.men.true.cov.100.15.25[2],
                  med.AD.num.women.true.cov.100.25.40[2], med.AD.num.men.true.cov.100.25.40[2],
                  med.AD.num.women.true.cov.100.40.50[2], med.AD.num.men.true.cov.100.40.50[2],
                  
                  
                  sd.AD.num.women.true.cov.100.15.25[2], sd.AD.num.men.true.cov.100.15.25[2],
                  sd.AD.num.women.true.cov.100.25.40[2], sd.AD.num.men.true.cov.100.25.40[2],
                  sd.AD.num.women.true.cov.100.40.50[2], sd.AD.num.men.true.cov.100.40.50[2])

AD.100.val.U <- c(mean.AD.num.women.true.cov.100.15.25[3], mean.AD.num.men.true.cov.100.15.25[3],
                  mean.AD.num.women.true.cov.100.25.40[3], mean.AD.num.men.true.cov.100.25.40[3],
                  mean.AD.num.women.true.cov.100.40.50[3], mean.AD.num.men.true.cov.100.40.50[3],
                  
                  
                  med.AD.num.women.true.cov.100.15.25[3], med.AD.num.men.true.cov.100.15.25[3],
                  med.AD.num.women.true.cov.100.25.40[3], med.AD.num.men.true.cov.100.25.40[3],
                  med.AD.num.women.true.cov.100.40.50[3], med.AD.num.men.true.cov.100.40.50[3],
                  
                  
                  sd.AD.num.women.true.cov.100.15.25[3], sd.AD.num.men.true.cov.100.15.25[3],
                  sd.AD.num.women.true.cov.100.25.40[3], sd.AD.num.men.true.cov.100.25.40[3],
                  sd.AD.num.women.true.cov.100.40.50[3], sd.AD.num.men.true.cov.100.40.50[3])



AD.100.val.L <- c(mean.AD.num.women.true.cov.100.15.25[1], mean.AD.num.men.true.cov.100.15.25[1],
                  mean.AD.num.women.true.cov.100.25.40[1], mean.AD.num.men.true.cov.100.25.40[1],
                  mean.AD.num.women.true.cov.100.40.50[1], mean.AD.num.men.true.cov.100.40.50[1],
                  
                  
                  med.AD.num.women.true.cov.100.15.25[1], med.AD.num.men.true.cov.100.15.25[1],
                  med.AD.num.women.true.cov.100.25.40[1], med.AD.num.men.true.cov.100.25.40[1],
                  med.AD.num.women.true.cov.100.40.50[1], med.AD.num.men.true.cov.100.40.50[1],
                  
                  
                  sd.AD.num.women.true.cov.100.15.25[1], sd.AD.num.men.true.cov.100.15.25[1],
                  sd.AD.num.women.true.cov.100.25.40[1], sd.AD.num.men.true.cov.100.25.40[1],
                  sd.AD.num.women.true.cov.100.40.50[1], sd.AD.num.men.true.cov.100.40.50[1])

names.AD <- c("mean.AD.women.cl.15.25", "mean.AD.men.cl.15.25", 
              "mean.AD.women.cl.25.40", "mean.AD.men.cl.25.40", 
              "mean.AD.women.cl.40.50", "mean.AD.men.cl.40.50",
              
              "med.AD.women.cl.15.25", "med.AD.men.cl.15.25",
              "med.AD.women.cl.25.40", "med.AD.men.cl.25.40",
              "med.AD.women.cl.40.50", "med.AD.men.cl.40.50",
              
              "sd.AD.women.cl.15.25", "sd.AD.men.cl.15.25",
              "sd.AD.women.cl.25.40", "sd.AD.men.cl.25.40",
              "sd.AD.women.cl.40.50", "sd.AD.men.cl.40.50")


AD_100 <- data.frame(names.AD, AD.100.val.L, AD.100.val.F, AD.100.val.U)

colnames(AD_100) <- c("name", "lower.Q1", "med", "upper.Q3")

AD_100 %>% 
  kable() %>% 
  kable_styling("striped") 
name lower.Q1 med upper.Q3
mean.AD.women.cl.15.25 11.407641 13.211441 15.024150
mean.AD.men.cl.15.25 2.311600 2.910712 3.602536
mean.AD.women.cl.25.40 11.842918 13.697393 15.582854
mean.AD.men.cl.25.40 10.410999 11.458548 12.440997
mean.AD.women.cl.40.50 3.427958 4.654275 7.064057
mean.AD.men.cl.40.50 19.171094 20.350407 21.330128
med.AD.women.cl.15.25 10.624465 13.064810 15.699235
med.AD.men.cl.15.25 1.912691 2.717614 3.524021
med.AD.women.cl.25.40 12.159128 14.566359 16.146174
med.AD.men.cl.25.40 10.275718 11.439360 12.647079
med.AD.women.cl.40.50 2.749416 4.346216 7.064057
med.AD.men.cl.40.50 19.137103 20.490878 21.654009
sd.AD.women.cl.15.25 6.245969 6.924548 7.641426
sd.AD.men.cl.15.25 1.439109 1.876773 2.245871
sd.AD.women.cl.25.40 3.498206 5.032506 6.243293
sd.AD.men.cl.25.40 3.724295 4.183783 4.629266
sd.AD.women.cl.40.50 1.978635 2.534065 3.847012
sd.AD.men.cl.40.50 3.199840 3.763976 4.440838
write.csv(AD_100, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_15_True_Age_Difference_100_Coverage.csv")

5.2 Age difference in different sequence coverage scenarios within pairings inferred from phylogenetic tree

AD.MAR.cov.35 <- d.MAR.cov.35 %>%
  select(contains(".AD.")) 
AD.MAR.cov.40 <- d.MAR.cov.40 %>%
  select(contains(".AD.")) 
AD.MAR.cov.45 <- d.MAR.cov.45 %>%
  select(contains(".AD.")) 
AD.MAR.cov.50 <- d.MAR.cov.50 %>%
  select(contains(".AD.")) 
AD.MAR.cov.55 <- d.MAR.cov.55 %>%
  select(contains(".AD.")) 
AD.MAR.cov.60 <- d.MAR.cov.60 %>%
  select(contains(".AD.")) 
AD.MAR.cov.65 <- d.MAR.cov.65 %>%
  select(contains(".AD.")) 
AD.MAR.cov.70 <- d.MAR.cov.70 %>%
  select(contains(".AD.")) 
AD.MAR.cov.75 <- d.MAR.cov.75 %>%
  select(contains(".AD.")) 
AD.MAR.cov.80 <- d.MAR.cov.80 %>%
  select(contains(".AD.")) 
AD.MAR.cov.85 <- d.MAR.cov.85 %>%
  select(contains(".AD.")) 
AD.MAR.cov.90 <- d.MAR.cov.90 %>%
  select(contains(".AD.")) 
AD.MAR.cov.95 <- d.MAR.cov.95 %>%
  select(contains(".AD.")) 

# Statistics of age difference  of individuals's true pairings at 100% coverage 


AD.true.cov.100 <- dr.cov.100 %>%
  select(contains(".AD.")) 



## Vector

# Mean

vector.mean.AD.num.women.true.cov.100.15.25 <- AD.true.cov.100[,1] # AD.true.cov.100[,1]
vector.mean.AD.num.men.true.cov.100.15.25 <- AD.true.cov.100[,2] # AD.true.cov.100[,2]

vector.mean.AD.num.women.true.cov.100.25.40 <- AD.true.cov.100[,3] # AD.true.cov.100[,3]
vector.mean.AD.num.men.true.cov.100.25.40 <- AD.true.cov.100[,4] # AD.true.cov.100[,4]

vector.mean.AD.num.women.true.cov.100.40.50 <- AD.true.cov.100[,5] # AD.true.cov.100[,5]
vector.mean.AD.num.men.true.cov.100.40.50 <- AD.true.cov.100[,6] # AD.true.cov.100[,6]

# Median

vector.med.AD.num.women.true.cov.100.15.25 <- AD.true.cov.100[,7] # AD.true.cov.100[,7]
vector.med.AD.num.men.true.cov.100.15.25 <- AD.true.cov.100[,8] # AD.true.cov.100[,8]

vector.med.AD.num.women.true.cov.100.25.40 <- AD.true.cov.100[,9] # AD.true.cov.100[,9]
vector.med.AD.num.men.true.cov.100.25.40 <- AD.true.cov.100[,10] # AD.true.cov.100[,10]

vector.med.AD.num.women.true.cov.100.40.50 <- AD.true.cov.100[,11] # AD.true.cov.100[,11]
vector.med.AD.num.men.true.cov.100.40.50 <- AD.true.cov.100[,12] # AD.true.cov.100[,12]

# Standard deviation

vector.sd.AD.num.women.true.cov.100.15.25 <- AD.true.cov.100[,13] # AD.true.cov.100[,13]
vector.sd.AD.num.men.true.cov.100.15.25 <- AD.true.cov.100[,14] # AD.true.cov.100[,14]

vector.sd.AD.num.women.true.cov.100.25.40 <- AD.true.cov.100[,15] # AD.true.cov.100[,15]
vector.sd.AD.num.men.true.cov.100.25.40 <- AD.true.cov.100[,16] # AD.true.cov.100[,16]

vector.sd.AD.num.women.true.cov.100.40.50 <- AD.true.cov.100[,17] # AD.true.cov.100[,17]
vector.sd.AD.num.men.true.cov.100.40.50 <- AD.true.cov.100[,18] # AD.true.cov.100[,18]



# Summarised

# Mean

mean.AD.num.women.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,1])
mean.AD.num.men.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,2])

mean.AD.num.women.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,3])
mean.AD.num.men.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,4])

mean.AD.num.women.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,5])
mean.AD.num.men.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,6])

# Median

med.AD.num.women.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,7])
med.AD.num.men.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,8])

med.AD.num.women.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,9])
med.AD.num.men.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,10])

med.AD.num.women.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,11])
med.AD.num.men.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,12])

# Standard deviation

sd.AD.num.women.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,13])
sd.AD.num.men.true.cov.100.15.25 <- quant.med(AD.true.cov.100[,14])

sd.AD.num.women.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,15])
sd.AD.num.men.true.cov.100.25.40 <- quant.med(AD.true.cov.100[,16])

sd.AD.num.women.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,17])
sd.AD.num.men.true.cov.100.40.50 <- quant.med(AD.true.cov.100[,18])




# Statistics of age difference  for individuals' in pairings from transmission clusters 

# Cov 35

## Vector

# Mean

vector.mean.MAR.cov.35.AD.women.cl.15.25 <- AD.MAR.cov.35[,1]
vector.mean.MAR.cov.35.AD.men.cl.15.25 <- AD.MAR.cov.35[,2]

vector.mean.MAR.cov.35.AD.women.cl.25.40 <- AD.MAR.cov.35[,3]
vector.mean.MAR.cov.35.AD.men.cl.25.40 <- AD.MAR.cov.35[,4]

vector.mean.MAR.cov.35.AD.women.cl.40.50 <- AD.MAR.cov.35[,5]
vector.mean.MAR.cov.35.AD.men.cl.40.50 <- AD.MAR.cov.35[,6]

# Median

vector.med.MAR.cov.35.AD.women.cl.15.25 <- AD.MAR.cov.35[,7]
vector.med.MAR.cov.35.AD.men.cl.15.25 <- AD.MAR.cov.35[,8]

vector.med.MAR.cov.35.AD.women.cl.25.40 <- AD.MAR.cov.35[,9]
vector.med.MAR.cov.35.AD.men.cl.25.40 <- AD.MAR.cov.35[,10]

vector.med.MAR.cov.35.AD.women.cl.40.50 <- AD.MAR.cov.35[,11]
vector.med.MAR.cov.35.AD.men.cl.40.50 <- AD.MAR.cov.35[,12]

# Standard deviation

vector.sd.MAR.cov.35.AD.women.cl.15.25 <- AD.MAR.cov.35[,13]
vector.sd.MAR.cov.35.AD.men.cl.15.25 <- AD.MAR.cov.35[,14]

vector.sd.MAR.cov.35.AD.women.cl.25.40 <- AD.MAR.cov.35[,15]
vector.sd.MAR.cov.35.AD.men.cl.25.40 <- AD.MAR.cov.35[,16]

vector.sd.MAR.cov.35.AD.women.cl.40.50 <- AD.MAR.cov.35[,17]
vector.sd.MAR.cov.35.AD.men.cl.40.50 <- AD.MAR.cov.35[,18]


## Summarised

# Mean

mean.MAR.cov.35.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.35[,1])
mean.MAR.cov.35.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.35[,2])

mean.MAR.cov.35.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.35[,3])
mean.MAR.cov.35.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.35[,4])

mean.MAR.cov.35.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.35[,5])
mean.MAR.cov.35.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.35[,6])

# Median

med.MAR.cov.35.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.35[,7])
med.MAR.cov.35.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.35[,8])

med.MAR.cov.35.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.35[,9])
med.MAR.cov.35.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.35[,10])

med.MAR.cov.35.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.35[,11])
med.MAR.cov.35.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.35[,12])

# Standard deviation

sd.MAR.cov.35.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.35[,13])
sd.MAR.cov.35.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.35[,14])

sd.MAR.cov.35.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.35[,15])
sd.MAR.cov.35.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.35[,16])

sd.MAR.cov.35.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.35[,17])
sd.MAR.cov.35.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.35[,18])


# Cov 40


## Vector

# Mean

vector.mean.MAR.cov.40.AD.women.cl.15.25 <- AD.MAR.cov.40[,1]
vector.mean.MAR.cov.40.AD.men.cl.15.25 <- AD.MAR.cov.40[,2]

vector.mean.MAR.cov.40.AD.women.cl.25.40 <- AD.MAR.cov.40[,3]
vector.mean.MAR.cov.40.AD.men.cl.25.40 <- AD.MAR.cov.40[,4]

vector.mean.MAR.cov.40.AD.women.cl.40.50 <- AD.MAR.cov.40[,5]
vector.mean.MAR.cov.40.AD.men.cl.40.50 <- AD.MAR.cov.40[,6]

# Median

vector.med.MAR.cov.40.AD.women.cl.15.25 <- AD.MAR.cov.40[,7]
vector.med.MAR.cov.40.AD.men.cl.15.25 <- AD.MAR.cov.40[,8]

vector.med.MAR.cov.40.AD.women.cl.25.40 <- AD.MAR.cov.40[,9]
vector.med.MAR.cov.40.AD.men.cl.25.40 <- AD.MAR.cov.40[,10]

vector.med.MAR.cov.40.AD.women.cl.40.50 <- AD.MAR.cov.40[,11]
vector.med.MAR.cov.40.AD.men.cl.40.50 <- AD.MAR.cov.40[,12]

# Standard deviation

vector.sd.MAR.cov.40.AD.women.cl.15.25 <- AD.MAR.cov.40[,13]
vector.sd.MAR.cov.40.AD.men.cl.15.25 <- AD.MAR.cov.40[,14]

vector.sd.MAR.cov.40.AD.women.cl.25.40 <- AD.MAR.cov.40[,15]
vector.sd.MAR.cov.40.AD.men.cl.25.40 <- AD.MAR.cov.40[,16]

vector.sd.MAR.cov.40.AD.women.cl.40.50 <- AD.MAR.cov.40[,17]
vector.sd.MAR.cov.40.AD.men.cl.40.50 <- AD.MAR.cov.40[,18]



## Summarised


# Mean

mean.MAR.cov.40.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.40[,1])
mean.MAR.cov.40.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.40[,2])

mean.MAR.cov.40.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.40[,3])
mean.MAR.cov.40.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.40[,4])

mean.MAR.cov.40.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.40[,5])
mean.MAR.cov.40.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.40[,6])

# Median

med.MAR.cov.40.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.40[,7])
med.MAR.cov.40.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.40[,8])

med.MAR.cov.40.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.40[,9])
med.MAR.cov.40.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.40[,10])

med.MAR.cov.40.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.40[,11])
med.MAR.cov.40.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.40[,12])

# Standard deviation

sd.MAR.cov.40.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.40[,13])
sd.MAR.cov.40.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.40[,14])

sd.MAR.cov.40.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.40[,15])
sd.MAR.cov.40.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.40[,16])

sd.MAR.cov.40.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.40[,17])
sd.MAR.cov.40.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.40[,18])



# Cov 45



# Vector


# Mean

vector.mean.MAR.cov.45.AD.women.cl.15.25 <- AD.MAR.cov.45[,1]
vector.mean.MAR.cov.45.AD.men.cl.15.25 <- AD.MAR.cov.45[,2]

vector.mean.MAR.cov.45.AD.women.cl.25.40 <- AD.MAR.cov.45[,3]
vector.mean.MAR.cov.45.AD.men.cl.25.40 <- AD.MAR.cov.45[,4]

vector.mean.MAR.cov.45.AD.women.cl.40.50 <- AD.MAR.cov.45[,5]
vector.mean.MAR.cov.45.AD.men.cl.40.50 <- AD.MAR.cov.45[,6]

# Median

vector.med.MAR.cov.45.AD.women.cl.15.25 <- AD.MAR.cov.45[,7]
vector.med.MAR.cov.45.AD.men.cl.15.25 <- AD.MAR.cov.45[,8]

vector.med.MAR.cov.45.AD.women.cl.25.40 <- AD.MAR.cov.45[,9]
vector.med.MAR.cov.45.AD.men.cl.25.40 <- AD.MAR.cov.45[,10]

vector.med.MAR.cov.45.AD.women.cl.40.50 <- AD.MAR.cov.45[,11]
vector.med.MAR.cov.45.AD.men.cl.40.50 <- AD.MAR.cov.45[,12]

# Standard deviation

vector.sd.MAR.cov.45.AD.women.cl.15.25 <- AD.MAR.cov.45[,13]
vector.sd.MAR.cov.45.AD.men.cl.15.25 <- AD.MAR.cov.45[,14]

vector.sd.MAR.cov.45.AD.women.cl.25.40 <- AD.MAR.cov.45[,15]
vector.sd.MAR.cov.45.AD.men.cl.25.40 <- AD.MAR.cov.45[,16]

vector.sd.MAR.cov.45.AD.women.cl.40.50 <- AD.MAR.cov.45[,17]
vector.sd.MAR.cov.45.AD.men.cl.40.50 <- AD.MAR.cov.45[,18]



## Summarised


# Mean

mean.MAR.cov.45.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.45[,1])
mean.MAR.cov.45.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.45[,2])

mean.MAR.cov.45.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.45[,3])
mean.MAR.cov.45.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.45[,4])

mean.MAR.cov.45.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.45[,5])
mean.MAR.cov.45.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.45[,6])

# Median

med.MAR.cov.45.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.45[,7])
med.MAR.cov.45.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.45[,8])

med.MAR.cov.45.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.45[,9])
med.MAR.cov.45.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.45[,10])

med.MAR.cov.45.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.45[,11])
med.MAR.cov.45.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.45[,12])

# Standard deviation

sd.MAR.cov.45.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.45[,13])
sd.MAR.cov.45.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.45[,14])

sd.MAR.cov.45.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.45[,15])
sd.MAR.cov.45.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.45[,16])

sd.MAR.cov.45.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.45[,17])
sd.MAR.cov.45.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.45[,18])


# Cov 50


## Vector


# Mean

vector.mean.MAR.cov.50.AD.women.cl.15.25 <- AD.MAR.cov.50[,1]
vector.mean.MAR.cov.50.AD.men.cl.15.25 <- AD.MAR.cov.50[,2]

vector.mean.MAR.cov.50.AD.women.cl.25.40 <- AD.MAR.cov.50[,3]
vector.mean.MAR.cov.50.AD.men.cl.25.40 <- AD.MAR.cov.50[,4]

vector.mean.MAR.cov.50.AD.women.cl.40.50 <- AD.MAR.cov.50[,5]
vector.mean.MAR.cov.50.AD.men.cl.40.50 <- AD.MAR.cov.50[,6]

# Median

vector.med.MAR.cov.50.AD.women.cl.15.25 <- AD.MAR.cov.50[,7]
vector.med.MAR.cov.50.AD.men.cl.15.25 <- AD.MAR.cov.50[,8]

vector.med.MAR.cov.50.AD.women.cl.25.40 <- AD.MAR.cov.50[,9]
vector.med.MAR.cov.50.AD.men.cl.25.40 <- AD.MAR.cov.50[,10]

vector.med.MAR.cov.50.AD.women.cl.40.50 <- AD.MAR.cov.50[,11]
vector.med.MAR.cov.50.AD.men.cl.40.50 <- AD.MAR.cov.50[,12]

# Standard deviation

vector.sd.MAR.cov.50.AD.women.cl.15.25 <- AD.MAR.cov.50[,13]
vector.sd.MAR.cov.50.AD.men.cl.15.25 <- AD.MAR.cov.50[,14]

vector.sd.MAR.cov.50.AD.women.cl.25.40 <- AD.MAR.cov.50[,15]
vector.sd.MAR.cov.50.AD.men.cl.25.40 <- AD.MAR.cov.50[,16]

vector.sd.MAR.cov.50.AD.women.cl.40.50 <- AD.MAR.cov.50[,17]
vector.sd.MAR.cov.50.AD.men.cl.40.50 <- AD.MAR.cov.50[,18]



## Summarised


# Mean

mean.MAR.cov.50.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.50[,1])
mean.MAR.cov.50.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.50[,2])

mean.MAR.cov.50.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.50[,3])
mean.MAR.cov.50.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.50[,4])

mean.MAR.cov.50.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.50[,5])
mean.MAR.cov.50.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.50[,6])

# Median

med.MAR.cov.50.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.50[,7])
med.MAR.cov.50.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.50[,8])

med.MAR.cov.50.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.50[,9])
med.MAR.cov.50.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.50[,10])

med.MAR.cov.50.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.50[,11])
med.MAR.cov.50.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.50[,12])

# Standard deviation

sd.MAR.cov.50.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.50[,13])
sd.MAR.cov.50.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.50[,14])

sd.MAR.cov.50.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.50[,15])
sd.MAR.cov.50.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.50[,16])

sd.MAR.cov.50.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.50[,17])
sd.MAR.cov.50.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.50[,18])



# Cov 55



## Vector


# Mean

vector.mean.MAR.cov.55.AD.women.cl.15.25 <- AD.MAR.cov.55[,1]
vector.mean.MAR.cov.55.AD.men.cl.15.25 <- AD.MAR.cov.55[,2]

vector.mean.MAR.cov.55.AD.women.cl.25.40 <- AD.MAR.cov.55[,3]
vector.mean.MAR.cov.55.AD.men.cl.25.40 <- AD.MAR.cov.55[,4]

vector.mean.MAR.cov.55.AD.women.cl.40.50 <- AD.MAR.cov.55[,5]
vector.mean.MAR.cov.55.AD.men.cl.40.50 <- AD.MAR.cov.55[,6]

# Median

vector.med.MAR.cov.55.AD.women.cl.15.25 <- AD.MAR.cov.55[,7]
vector.med.MAR.cov.55.AD.men.cl.15.25 <- AD.MAR.cov.55[,8]

vector.med.MAR.cov.55.AD.women.cl.25.40 <- AD.MAR.cov.55[,9]
vector.med.MAR.cov.55.AD.men.cl.25.40 <- AD.MAR.cov.55[,10]

vector.med.MAR.cov.55.AD.women.cl.40.50 <- AD.MAR.cov.55[,11]
vector.med.MAR.cov.55.AD.men.cl.40.50 <- AD.MAR.cov.55[,12]

# Standard deviation

vector.sd.MAR.cov.55.AD.women.cl.15.25 <- AD.MAR.cov.55[,13]
vector.sd.MAR.cov.55.AD.men.cl.15.25 <- AD.MAR.cov.55[,14]

vector.sd.MAR.cov.55.AD.women.cl.25.40 <- AD.MAR.cov.55[,15]
vector.sd.MAR.cov.55.AD.men.cl.25.40 <- AD.MAR.cov.55[,16]

vector.sd.MAR.cov.55.AD.women.cl.40.50 <- AD.MAR.cov.55[,17]
vector.sd.MAR.cov.55.AD.men.cl.40.50 <- AD.MAR.cov.55[,18]



## Summarised


# Mean

mean.MAR.cov.55.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.55[,1])
mean.MAR.cov.55.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.55[,2])

mean.MAR.cov.55.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.55[,3])
mean.MAR.cov.55.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.55[,4])

mean.MAR.cov.55.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.55[,5])
mean.MAR.cov.55.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.55[,6])

# Median

med.MAR.cov.55.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.55[,7])
med.MAR.cov.55.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.55[,8])

med.MAR.cov.55.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.55[,9])
med.MAR.cov.55.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.55[,10])

med.MAR.cov.55.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.55[,11])
med.MAR.cov.55.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.55[,12])

# Standard deviation

sd.MAR.cov.55.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.55[,13])
sd.MAR.cov.55.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.55[,14])

sd.MAR.cov.55.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.55[,15])
sd.MAR.cov.55.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.55[,16])

sd.MAR.cov.55.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.55[,17])
sd.MAR.cov.55.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.55[,18])



# Cov 60


## Vector

# Mean

vector.mean.MAR.cov.60.AD.women.cl.15.25 <- AD.MAR.cov.60[,1]
vector.mean.MAR.cov.60.AD.men.cl.15.25 <- AD.MAR.cov.60[,2]

vector.mean.MAR.cov.60.AD.women.cl.25.40 <- AD.MAR.cov.60[,3]
vector.mean.MAR.cov.60.AD.men.cl.25.40 <- AD.MAR.cov.60[,4]

vector.mean.MAR.cov.60.AD.women.cl.40.50 <- AD.MAR.cov.60[,5]
vector.mean.MAR.cov.60.AD.men.cl.40.50 <- AD.MAR.cov.60[,6]

# Median

vector.med.MAR.cov.60.AD.women.cl.15.25 <- AD.MAR.cov.60[,7]
vector.med.MAR.cov.60.AD.men.cl.15.25 <- AD.MAR.cov.60[,8]

vector.med.MAR.cov.60.AD.women.cl.25.40 <- AD.MAR.cov.60[,9]
vector.med.MAR.cov.60.AD.men.cl.25.40 <- AD.MAR.cov.60[,10]

vector.med.MAR.cov.60.AD.women.cl.40.50 <- AD.MAR.cov.60[,11]
vector.med.MAR.cov.60.AD.men.cl.40.50 <- AD.MAR.cov.60[,12]

# Standard deviation

vector.sd.MAR.cov.60.AD.women.cl.15.25 <- AD.MAR.cov.60[,13]
vector.sd.MAR.cov.60.AD.men.cl.15.25 <- AD.MAR.cov.60[,14]

vector.sd.MAR.cov.60.AD.women.cl.25.40 <- AD.MAR.cov.60[,15]
vector.sd.MAR.cov.60.AD.men.cl.25.40 <- AD.MAR.cov.60[,16]

vector.sd.MAR.cov.60.AD.women.cl.40.50 <- AD.MAR.cov.60[,17]
vector.sd.MAR.cov.60.AD.men.cl.40.50 <- AD.MAR.cov.60[,18]



## Summarised


# Mean

mean.MAR.cov.60.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.60[,1])
mean.MAR.cov.60.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.60[,2])

mean.MAR.cov.60.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.60[,3])
mean.MAR.cov.60.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.60[,4])

mean.MAR.cov.60.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.60[,5])
mean.MAR.cov.60.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.60[,6])

# Median

med.MAR.cov.60.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.60[,7])
med.MAR.cov.60.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.60[,8])

med.MAR.cov.60.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.60[,9])
med.MAR.cov.60.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.60[,10])

med.MAR.cov.60.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.60[,11])
med.MAR.cov.60.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.60[,12])

# Standard deviation

sd.MAR.cov.60.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.60[,13])
sd.MAR.cov.60.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.60[,14])

sd.MAR.cov.60.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.60[,15])
sd.MAR.cov.60.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.60[,16])

sd.MAR.cov.60.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.60[,17])
sd.MAR.cov.60.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.60[,18])



# Cov 65



## Vector

# Mean

vector.mean.MAR.cov.65.AD.women.cl.15.25 <- AD.MAR.cov.65[,1]
vector.mean.MAR.cov.65.AD.men.cl.15.25 <- AD.MAR.cov.65[,2]

vector.mean.MAR.cov.65.AD.women.cl.25.40 <- AD.MAR.cov.65[,3]
vector.mean.MAR.cov.65.AD.men.cl.25.40 <- AD.MAR.cov.65[,4]

vector.mean.MAR.cov.65.AD.women.cl.40.50 <- AD.MAR.cov.65[,5]
vector.mean.MAR.cov.65.AD.men.cl.40.50 <- AD.MAR.cov.65[,6]

# Median

vector.med.MAR.cov.65.AD.women.cl.15.25 <- AD.MAR.cov.65[,7]
vector.med.MAR.cov.65.AD.men.cl.15.25 <- AD.MAR.cov.65[,8]

vector.med.MAR.cov.65.AD.women.cl.25.40 <- AD.MAR.cov.65[,9]
vector.med.MAR.cov.65.AD.men.cl.25.40 <- AD.MAR.cov.65[,10]

vector.med.MAR.cov.65.AD.women.cl.40.50 <- AD.MAR.cov.65[,11]
vector.med.MAR.cov.65.AD.men.cl.40.50 <- AD.MAR.cov.65[,12]

# Standard deviation

vector.sd.MAR.cov.65.AD.women.cl.15.25 <- AD.MAR.cov.65[,13]
vector.sd.MAR.cov.65.AD.men.cl.15.25 <- AD.MAR.cov.65[,14]

vector.sd.MAR.cov.65.AD.women.cl.25.40 <- AD.MAR.cov.65[,15]
vector.sd.MAR.cov.65.AD.men.cl.25.40 <- AD.MAR.cov.65[,16]

vector.sd.MAR.cov.65.AD.women.cl.40.50 <- AD.MAR.cov.65[,17]
vector.sd.MAR.cov.65.AD.men.cl.40.50 <- AD.MAR.cov.65[,18]



## Summarised


# Mean

mean.MAR.cov.65.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.65[,1])
mean.MAR.cov.65.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.65[,2])

mean.MAR.cov.65.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.65[,3])
mean.MAR.cov.65.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.65[,4])

mean.MAR.cov.65.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.65[,5])
mean.MAR.cov.65.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.65[,6])

# Median

med.MAR.cov.65.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.65[,7])
med.MAR.cov.65.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.65[,8])

med.MAR.cov.65.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.65[,9])
med.MAR.cov.65.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.65[,10])

med.MAR.cov.65.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.65[,11])
med.MAR.cov.65.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.65[,12])

# Standard deviation

sd.MAR.cov.65.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.65[,13])
sd.MAR.cov.65.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.65[,14])

sd.MAR.cov.65.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.65[,15])
sd.MAR.cov.65.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.65[,16])

sd.MAR.cov.65.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.65[,17])
sd.MAR.cov.65.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.65[,18])



# Cov 70



## Vector


# Mean

vector.mean.MAR.cov.70.AD.women.cl.15.25 <- AD.MAR.cov.70[,1]
vector.mean.MAR.cov.70.AD.men.cl.15.25 <- AD.MAR.cov.70[,2]

vector.mean.MAR.cov.70.AD.women.cl.25.40 <- AD.MAR.cov.70[,3]
vector.mean.MAR.cov.70.AD.men.cl.25.40 <- AD.MAR.cov.70[,4]

vector.mean.MAR.cov.70.AD.women.cl.40.50 <- AD.MAR.cov.70[,5]
vector.mean.MAR.cov.70.AD.men.cl.40.50 <- AD.MAR.cov.70[,6]

# Median

vector.med.MAR.cov.70.AD.women.cl.15.25 <- AD.MAR.cov.70[,7]
vector.med.MAR.cov.70.AD.men.cl.15.25 <- AD.MAR.cov.70[,8]

vector.med.MAR.cov.70.AD.women.cl.25.40 <- AD.MAR.cov.70[,9]
vector.med.MAR.cov.70.AD.men.cl.25.40 <- AD.MAR.cov.70[,10]

vector.med.MAR.cov.70.AD.women.cl.40.50 <- AD.MAR.cov.70[,11]
vector.med.MAR.cov.70.AD.men.cl.40.50 <- AD.MAR.cov.70[,12]

# Standard deviation

vector.sd.MAR.cov.70.AD.women.cl.15.25 <- AD.MAR.cov.70[,13]
vector.sd.MAR.cov.70.AD.men.cl.15.25 <- AD.MAR.cov.70[,14]

vector.sd.MAR.cov.70.AD.women.cl.25.40 <- AD.MAR.cov.70[,15]
vector.sd.MAR.cov.70.AD.men.cl.25.40 <- AD.MAR.cov.70[,16]

vector.sd.MAR.cov.70.AD.women.cl.40.50 <- AD.MAR.cov.70[,17]
vector.sd.MAR.cov.70.AD.men.cl.40.50 <- AD.MAR.cov.70[,18]




## Summarised


# Mean

mean.MAR.cov.70.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.70[,1])
mean.MAR.cov.70.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.70[,2])

mean.MAR.cov.70.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.70[,3])
mean.MAR.cov.70.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.70[,4])

mean.MAR.cov.70.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.70[,5])
mean.MAR.cov.70.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.70[,6])

# Median

med.MAR.cov.70.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.70[,7])
med.MAR.cov.70.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.70[,8])

med.MAR.cov.70.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.70[,9])
med.MAR.cov.70.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.70[,10])

med.MAR.cov.70.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.70[,11])
med.MAR.cov.70.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.70[,12])

# Standard deviation

sd.MAR.cov.70.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.70[,13])
sd.MAR.cov.70.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.70[,14])

sd.MAR.cov.70.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.70[,15])
sd.MAR.cov.70.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.70[,16])

sd.MAR.cov.70.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.70[,17])
sd.MAR.cov.70.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.70[,18])



# Cov 75


## Vector


# Mean

vector.mean.MAR.cov.75.AD.women.cl.15.25 <- AD.MAR.cov.75[,1]
vector.mean.MAR.cov.75.AD.men.cl.15.25 <- AD.MAR.cov.75[,2]

vector.mean.MAR.cov.75.AD.women.cl.25.40 <- AD.MAR.cov.75[,3]
vector.mean.MAR.cov.75.AD.men.cl.25.40 <- AD.MAR.cov.75[,4]

vector.mean.MAR.cov.75.AD.women.cl.40.50 <- AD.MAR.cov.75[,5]
vector.mean.MAR.cov.75.AD.men.cl.40.50 <- AD.MAR.cov.75[,6]

# Median

vector.med.MAR.cov.75.AD.women.cl.15.25 <- AD.MAR.cov.75[,7]
vector.med.MAR.cov.75.AD.men.cl.15.25 <- AD.MAR.cov.75[,8]

vector.med.MAR.cov.75.AD.women.cl.25.40 <- AD.MAR.cov.75[,9]
vector.med.MAR.cov.75.AD.men.cl.25.40 <- AD.MAR.cov.75[,10]

vector.med.MAR.cov.75.AD.women.cl.40.50 <- AD.MAR.cov.75[,11]
vector.med.MAR.cov.75.AD.men.cl.40.50 <- AD.MAR.cov.75[,12]

# Standard deviation

vector.sd.MAR.cov.75.AD.women.cl.15.25 <- AD.MAR.cov.75[,13]
vector.sd.MAR.cov.75.AD.men.cl.15.25 <- AD.MAR.cov.75[,14]

vector.sd.MAR.cov.75.AD.women.cl.25.40 <- AD.MAR.cov.75[,15]
vector.sd.MAR.cov.75.AD.men.cl.25.40 <- AD.MAR.cov.75[,16]

vector.sd.MAR.cov.75.AD.women.cl.40.50 <- AD.MAR.cov.75[,17]
vector.sd.MAR.cov.75.AD.men.cl.40.50 <- AD.MAR.cov.75[,18]


## Summarised


# Mean

mean.MAR.cov.75.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.75[,1])
mean.MAR.cov.75.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.75[,2])

mean.MAR.cov.75.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.75[,3])
mean.MAR.cov.75.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.75[,4])

mean.MAR.cov.75.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.75[,5])
mean.MAR.cov.75.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.75[,6])

# Median

med.MAR.cov.75.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.75[,7])
med.MAR.cov.75.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.75[,8])

med.MAR.cov.75.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.75[,9])
med.MAR.cov.75.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.75[,10])

med.MAR.cov.75.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.75[,11])
med.MAR.cov.75.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.75[,12])

# Standard deviation

sd.MAR.cov.75.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.75[,13])
sd.MAR.cov.75.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.75[,14])

sd.MAR.cov.75.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.75[,15])
sd.MAR.cov.75.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.75[,16])

sd.MAR.cov.75.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.75[,17])
sd.MAR.cov.75.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.75[,18])



# Cov 80


## Vector

# Mean

vector.mean.MAR.cov.80.AD.women.cl.15.25 <- AD.MAR.cov.80[,1]
vector.mean.MAR.cov.80.AD.men.cl.15.25 <- AD.MAR.cov.80[,2]

vector.mean.MAR.cov.80.AD.women.cl.25.40 <- AD.MAR.cov.80[,3]
vector.mean.MAR.cov.80.AD.men.cl.25.40 <- AD.MAR.cov.80[,4]

vector.mean.MAR.cov.80.AD.women.cl.40.50 <- AD.MAR.cov.80[,5]
vector.mean.MAR.cov.80.AD.men.cl.40.50 <- AD.MAR.cov.80[,6]

# Median

vector.med.MAR.cov.80.AD.women.cl.15.25 <- AD.MAR.cov.80[,7]
vector.med.MAR.cov.80.AD.men.cl.15.25 <- AD.MAR.cov.80[,8]

vector.med.MAR.cov.80.AD.women.cl.25.40 <- AD.MAR.cov.80[,9]
vector.med.MAR.cov.80.AD.men.cl.25.40 <- AD.MAR.cov.80[,10]

vector.med.MAR.cov.80.AD.women.cl.40.50 <- AD.MAR.cov.80[,11]
vector.med.MAR.cov.80.AD.men.cl.40.50 <- AD.MAR.cov.80[,12]

# Standard deviation

vector.sd.MAR.cov.80.AD.women.cl.15.25 <- AD.MAR.cov.80[,13]
vector.sd.MAR.cov.80.AD.men.cl.15.25 <- AD.MAR.cov.80[,14]

vector.sd.MAR.cov.80.AD.women.cl.25.40 <- AD.MAR.cov.80[,15]
vector.sd.MAR.cov.80.AD.men.cl.25.40 <- AD.MAR.cov.80[,16]

vector.sd.MAR.cov.80.AD.women.cl.40.50 <- AD.MAR.cov.80[,17]
vector.sd.MAR.cov.80.AD.men.cl.40.50 <- AD.MAR.cov.80[,18]


## Summarised


# Mean

mean.MAR.cov.80.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.80[,1])
mean.MAR.cov.80.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.80[,2])

mean.MAR.cov.80.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.80[,3])
mean.MAR.cov.80.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.80[,4])

mean.MAR.cov.80.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.80[,5])
mean.MAR.cov.80.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.80[,6])

# Median

med.MAR.cov.80.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.80[,7])
med.MAR.cov.80.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.80[,8])

med.MAR.cov.80.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.80[,9])
med.MAR.cov.80.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.80[,10])

med.MAR.cov.80.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.80[,11])
med.MAR.cov.80.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.80[,12])

# Standard deviation

sd.MAR.cov.80.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.80[,13])
sd.MAR.cov.80.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.80[,14])

sd.MAR.cov.80.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.80[,15])
sd.MAR.cov.80.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.80[,16])

sd.MAR.cov.80.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.80[,17])
sd.MAR.cov.80.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.80[,18])



# Cov 85


## Vector

# Mean

vector.mean.MAR.cov.85.AD.women.cl.15.25 <- AD.MAR.cov.85[,1]
vector.mean.MAR.cov.85.AD.men.cl.15.25 <- AD.MAR.cov.85[,2]

vector.mean.MAR.cov.85.AD.women.cl.25.40 <- AD.MAR.cov.85[,3]
vector.mean.MAR.cov.85.AD.men.cl.25.40 <- AD.MAR.cov.85[,4]

vector.mean.MAR.cov.85.AD.women.cl.40.50 <- AD.MAR.cov.85[,5]
vector.mean.MAR.cov.85.AD.men.cl.40.50 <- AD.MAR.cov.85[,6]

# Median

vector.med.MAR.cov.85.AD.women.cl.15.25 <- AD.MAR.cov.85[,7]
vector.med.MAR.cov.85.AD.men.cl.15.25 <- AD.MAR.cov.85[,8]

vector.med.MAR.cov.85.AD.women.cl.25.40 <- AD.MAR.cov.85[,9]
vector.med.MAR.cov.85.AD.men.cl.25.40 <- AD.MAR.cov.85[,10]

vector.med.MAR.cov.85.AD.women.cl.40.50 <- AD.MAR.cov.85[,11]
vector.med.MAR.cov.85.AD.men.cl.40.50 <- AD.MAR.cov.85[,12]

# Standard deviation

vector.sd.MAR.cov.85.AD.women.cl.15.25 <- AD.MAR.cov.85[,13]
vector.sd.MAR.cov.85.AD.men.cl.15.25 <- AD.MAR.cov.85[,14]

vector.sd.MAR.cov.85.AD.women.cl.25.40 <- AD.MAR.cov.85[,15]
vector.sd.MAR.cov.85.AD.men.cl.25.40 <- AD.MAR.cov.85[,16]

vector.sd.MAR.cov.85.AD.women.cl.40.50 <- AD.MAR.cov.85[,17]
vector.sd.MAR.cov.85.AD.men.cl.40.50 <- AD.MAR.cov.85[,18]


## Summarised


# Mean

mean.MAR.cov.85.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.85[,1])
mean.MAR.cov.85.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.85[,2])

mean.MAR.cov.85.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.85[,3])
mean.MAR.cov.85.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.85[,4])

mean.MAR.cov.85.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.85[,5])
mean.MAR.cov.85.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.85[,6])

# Median

med.MAR.cov.85.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.85[,7])
med.MAR.cov.85.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.85[,8])

med.MAR.cov.85.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.85[,9])
med.MAR.cov.85.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.85[,10])

med.MAR.cov.85.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.85[,11])
med.MAR.cov.85.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.85[,12])

# Standard deviation

sd.MAR.cov.85.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.85[,13])
sd.MAR.cov.85.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.85[,14])

sd.MAR.cov.85.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.85[,15])
sd.MAR.cov.85.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.85[,16])

sd.MAR.cov.85.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.85[,17])
sd.MAR.cov.85.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.85[,18])



# Cov 90


## Vector

# Mean

vector.mean.MAR.cov.90.AD.women.cl.15.25 <- AD.MAR.cov.90[,1]
vector.mean.MAR.cov.90.AD.men.cl.15.25 <- AD.MAR.cov.90[,2]

vector.mean.MAR.cov.90.AD.women.cl.25.40 <- AD.MAR.cov.90[,3]
vector.mean.MAR.cov.90.AD.men.cl.25.40 <- AD.MAR.cov.90[,4]

vector.mean.MAR.cov.90.AD.women.cl.40.50 <- AD.MAR.cov.90[,5]
vector.mean.MAR.cov.90.AD.men.cl.40.50 <- AD.MAR.cov.90[,6]

# Median

vector.med.MAR.cov.90.AD.women.cl.15.25 <- AD.MAR.cov.90[,7]
vector.med.MAR.cov.90.AD.men.cl.15.25 <- AD.MAR.cov.90[,8]

vector.med.MAR.cov.90.AD.women.cl.25.40 <- AD.MAR.cov.90[,9]
vector.med.MAR.cov.90.AD.men.cl.25.40 <- AD.MAR.cov.90[,10]

vector.med.MAR.cov.90.AD.women.cl.40.50 <- AD.MAR.cov.90[,11]
vector.med.MAR.cov.90.AD.men.cl.40.50 <- AD.MAR.cov.90[,12]

# Standard deviation

vector.sd.MAR.cov.90.AD.women.cl.15.25 <- AD.MAR.cov.90[,13]
vector.sd.MAR.cov.90.AD.men.cl.15.25 <- AD.MAR.cov.90[,14]

vector.sd.MAR.cov.90.AD.women.cl.25.40 <- AD.MAR.cov.90[,15]
vector.sd.MAR.cov.90.AD.men.cl.25.40 <- AD.MAR.cov.90[,16]

vector.sd.MAR.cov.90.AD.women.cl.40.50 <- AD.MAR.cov.90[,17]
vector.sd.MAR.cov.90.AD.men.cl.40.50 <- AD.MAR.cov.90[,18]



## Summarised


# Mean

mean.MAR.cov.90.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.90[,1])
mean.MAR.cov.90.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.90[,2])

mean.MAR.cov.90.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.90[,3])
mean.MAR.cov.90.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.90[,4])

mean.MAR.cov.90.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.90[,5])
mean.MAR.cov.90.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.90[,6])

# Median

med.MAR.cov.90.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.90[,7])
med.MAR.cov.90.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.90[,8])

med.MAR.cov.90.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.90[,9])
med.MAR.cov.90.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.90[,10])

med.MAR.cov.90.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.90[,11])
med.MAR.cov.90.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.90[,12])

# Standard deviation

sd.MAR.cov.90.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.90[,13])
sd.MAR.cov.90.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.90[,14])

sd.MAR.cov.90.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.90[,15])
sd.MAR.cov.90.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.90[,16])

sd.MAR.cov.90.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.90[,17])
sd.MAR.cov.90.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.90[,18])




# Cov 95


## Vector

# Mean

vector.mean.MAR.cov.95.AD.women.cl.15.25 <- AD.MAR.cov.95[,1]
vector.mean.MAR.cov.95.AD.men.cl.15.25 <- AD.MAR.cov.95[,2]

vector.mean.MAR.cov.95.AD.women.cl.25.40 <- AD.MAR.cov.95[,3]
vector.mean.MAR.cov.95.AD.men.cl.25.40 <- AD.MAR.cov.95[,4]

vector.mean.MAR.cov.95.AD.women.cl.40.50 <- AD.MAR.cov.95[,5]
vector.mean.MAR.cov.95.AD.men.cl.40.50 <- AD.MAR.cov.95[,6]

# Median

vector.med.MAR.cov.95.AD.women.cl.15.25 <- AD.MAR.cov.95[,7]
vector.med.MAR.cov.95.AD.men.cl.15.25 <- AD.MAR.cov.95[,8]

vector.med.MAR.cov.95.AD.women.cl.25.40 <- AD.MAR.cov.95[,9]
vector.med.MAR.cov.95.AD.men.cl.25.40 <- AD.MAR.cov.95[,10]

vector.med.MAR.cov.95.AD.women.cl.40.50 <- AD.MAR.cov.95[,11]
vector.med.MAR.cov.95.AD.men.cl.40.50 <- AD.MAR.cov.95[,12]

# Standard deviation

vector.sd.MAR.cov.95.AD.women.cl.15.25 <- AD.MAR.cov.95[,13]
vector.sd.MAR.cov.95.AD.men.cl.15.25 <- AD.MAR.cov.95[,14]

vector.sd.MAR.cov.95.AD.women.cl.25.40 <- AD.MAR.cov.95[,15]
vector.sd.MAR.cov.95.AD.men.cl.25.40 <- AD.MAR.cov.95[,16]

vector.sd.MAR.cov.95.AD.women.cl.40.50 <- AD.MAR.cov.95[,17]
vector.sd.MAR.cov.95.AD.men.cl.40.50 <- AD.MAR.cov.95[,18]


## Summarised

# Mean

mean.MAR.cov.95.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.95[,1])
mean.MAR.cov.95.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.95[,2])

mean.MAR.cov.95.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.95[,3])
mean.MAR.cov.95.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.95[,4])

mean.MAR.cov.95.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.95[,5])
mean.MAR.cov.95.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.95[,6])

# Median

med.MAR.cov.95.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.95[,7])
med.MAR.cov.95.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.95[,8])

med.MAR.cov.95.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.95[,9])
med.MAR.cov.95.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.95[,10])

med.MAR.cov.95.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.95[,11])
med.MAR.cov.95.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.95[,12])

# Standard deviation

sd.MAR.cov.95.AD.women.cl.15.25 <- quant.med(AD.MAR.cov.95[,13])
sd.MAR.cov.95.AD.men.cl.15.25 <- quant.med(AD.MAR.cov.95[,14])

sd.MAR.cov.95.AD.women.cl.25.40 <- quant.med(AD.MAR.cov.95[,15])
sd.MAR.cov.95.AD.men.cl.25.40 <- quant.med(AD.MAR.cov.95[,16])

sd.MAR.cov.95.AD.women.cl.40.50 <- quant.med(AD.MAR.cov.95[,17])
sd.MAR.cov.95.AD.men.cl.40.50 <- quant.med(AD.MAR.cov.95[,18])



# Table of AD statistics inferred from transmission clusters --------


AD.stats <- matrix(c(mean.MAR.cov.35.AD.women.cl.15.25[2], mean.MAR.cov.40.AD.women.cl.15.25[2], 
                     mean.MAR.cov.45.AD.women.cl.15.25[2], mean.MAR.cov.50.AD.women.cl.15.25[2], 
                     mean.MAR.cov.55.AD.women.cl.15.25[2], mean.MAR.cov.60.AD.women.cl.15.25[2], 
                     mean.MAR.cov.65.AD.women.cl.15.25[2], mean.MAR.cov.70.AD.women.cl.15.25[2], 
                     mean.MAR.cov.75.AD.women.cl.15.25[2], mean.MAR.cov.80.AD.women.cl.15.25[2], 
                     mean.MAR.cov.85.AD.women.cl.15.25[2], mean.MAR.cov.90.AD.women.cl.15.25[2], 
                     mean.MAR.cov.95.AD.women.cl.15.25[2], mean.AD.num.women.true.cov.100.15.25[2],
                     
                     mean.MAR.cov.35.AD.men.cl.15.25[2], mean.MAR.cov.40.AD.men.cl.15.25[2], 
                     mean.MAR.cov.45.AD.men.cl.15.25[2], mean.MAR.cov.50.AD.men.cl.15.25[2], 
                     mean.MAR.cov.55.AD.men.cl.15.25[2], mean.MAR.cov.60.AD.men.cl.15.25[2], 
                     mean.MAR.cov.65.AD.men.cl.15.25[2], mean.MAR.cov.70.AD.men.cl.15.25[2], 
                     mean.MAR.cov.75.AD.men.cl.15.25[2], mean.MAR.cov.80.AD.men.cl.15.25[2], 
                     mean.MAR.cov.85.AD.men.cl.15.25[2], mean.MAR.cov.90.AD.men.cl.15.25[2], 
                     mean.MAR.cov.95.AD.men.cl.15.25[2], mean.AD.num.men.true.cov.100.15.25[2],
                     
                     mean.MAR.cov.35.AD.women.cl.25.40[2], mean.MAR.cov.40.AD.women.cl.25.40[2], 
                     mean.MAR.cov.45.AD.women.cl.25.40[2], mean.MAR.cov.50.AD.women.cl.25.40[2], 
                     mean.MAR.cov.55.AD.women.cl.25.40[2], mean.MAR.cov.60.AD.women.cl.25.40[2], 
                     mean.MAR.cov.65.AD.women.cl.25.40[2], mean.MAR.cov.70.AD.women.cl.25.40[2], 
                     mean.MAR.cov.75.AD.women.cl.25.40[2], mean.MAR.cov.80.AD.women.cl.25.40[2], 
                     mean.MAR.cov.85.AD.women.cl.25.40[2], mean.MAR.cov.90.AD.women.cl.25.40[2], 
                     mean.MAR.cov.95.AD.women.cl.25.40[2], mean.AD.num.women.true.cov.100.25.40[2],
                     
                     mean.MAR.cov.35.AD.men.cl.25.40[2], mean.MAR.cov.40.AD.men.cl.25.40[2], 
                     mean.MAR.cov.45.AD.men.cl.25.40[2], mean.MAR.cov.50.AD.men.cl.25.40[2], 
                     mean.MAR.cov.55.AD.men.cl.25.40[2], mean.MAR.cov.60.AD.men.cl.25.40[2], 
                     mean.MAR.cov.65.AD.men.cl.25.40[2], mean.MAR.cov.70.AD.men.cl.25.40[2], 
                     mean.MAR.cov.75.AD.men.cl.25.40[2], mean.MAR.cov.80.AD.men.cl.25.40[2], 
                     mean.MAR.cov.85.AD.men.cl.25.40[2], mean.MAR.cov.90.AD.men.cl.25.40[2], 
                     mean.MAR.cov.95.AD.men.cl.25.40[2], mean.AD.num.men.true.cov.100.25.40[2],
                     
                     mean.MAR.cov.35.AD.women.cl.40.50[2], mean.MAR.cov.40.AD.women.cl.40.50[2], 
                     mean.MAR.cov.45.AD.women.cl.40.50[2], mean.MAR.cov.50.AD.women.cl.40.50[2], 
                     mean.MAR.cov.55.AD.women.cl.40.50[2], mean.MAR.cov.60.AD.women.cl.40.50[2], 
                     mean.MAR.cov.65.AD.women.cl.40.50[2], mean.MAR.cov.70.AD.women.cl.40.50[2], 
                     mean.MAR.cov.75.AD.women.cl.40.50[2], mean.MAR.cov.80.AD.women.cl.40.50[2], 
                     mean.MAR.cov.85.AD.women.cl.40.50[2], mean.MAR.cov.90.AD.women.cl.40.50[2], 
                     mean.MAR.cov.95.AD.women.cl.40.50[2], mean.AD.num.women.true.cov.100.40.50[2],
                     
                     mean.MAR.cov.35.AD.men.cl.40.50[2], mean.MAR.cov.40.AD.men.cl.40.50[2], 
                     mean.MAR.cov.45.AD.men.cl.40.50[2], mean.MAR.cov.50.AD.men.cl.40.50[2], 
                     mean.MAR.cov.55.AD.men.cl.40.50[2], mean.MAR.cov.60.AD.men.cl.40.50[2], 
                     mean.MAR.cov.65.AD.men.cl.40.50[2], mean.MAR.cov.70.AD.men.cl.40.50[2], 
                     mean.MAR.cov.75.AD.men.cl.40.50[2], mean.MAR.cov.80.AD.men.cl.40.50[2], 
                     mean.MAR.cov.85.AD.men.cl.40.50[2], mean.MAR.cov.90.AD.men.cl.40.50[2], 
                     mean.MAR.cov.95.AD.men.cl.40.50[2], mean.AD.num.men.true.cov.100.40.50[2],
                     
                     med.MAR.cov.35.AD.women.cl.15.25[2], med.MAR.cov.40.AD.women.cl.15.25[2], 
                     med.MAR.cov.45.AD.women.cl.15.25[2], med.MAR.cov.50.AD.women.cl.15.25[2], 
                     med.MAR.cov.55.AD.women.cl.15.25[2], med.MAR.cov.60.AD.women.cl.15.25[2], 
                     med.MAR.cov.65.AD.women.cl.15.25[2], med.MAR.cov.70.AD.women.cl.15.25[2], 
                     med.MAR.cov.75.AD.women.cl.15.25[2], med.MAR.cov.80.AD.women.cl.15.25[2], 
                     med.MAR.cov.85.AD.women.cl.15.25[2], med.MAR.cov.90.AD.women.cl.15.25[2], 
                     med.MAR.cov.95.AD.women.cl.15.25[2], med.AD.num.women.true.cov.100.15.25[2],
                     
                     med.MAR.cov.35.AD.men.cl.15.25[2], med.MAR.cov.40.AD.men.cl.15.25[2], 
                     med.MAR.cov.45.AD.men.cl.15.25[2], med.MAR.cov.50.AD.men.cl.15.25[2], 
                     med.MAR.cov.55.AD.men.cl.15.25[2], med.MAR.cov.60.AD.men.cl.15.25[2], 
                     med.MAR.cov.65.AD.men.cl.15.25[2], med.MAR.cov.70.AD.men.cl.15.25[2], 
                     med.MAR.cov.75.AD.men.cl.15.25[2], med.MAR.cov.80.AD.men.cl.15.25[2], 
                     med.MAR.cov.85.AD.men.cl.15.25[2], med.MAR.cov.90.AD.men.cl.15.25[2], 
                     med.MAR.cov.95.AD.men.cl.15.25[2], med.AD.num.men.true.cov.100.15.25[2],
                     
                     med.MAR.cov.35.AD.women.cl.25.40[2], med.MAR.cov.40.AD.women.cl.25.40[2], 
                     med.MAR.cov.45.AD.women.cl.25.40[2], med.MAR.cov.50.AD.women.cl.25.40[2], 
                     med.MAR.cov.55.AD.women.cl.25.40[2], med.MAR.cov.60.AD.women.cl.25.40[2], 
                     med.MAR.cov.65.AD.women.cl.25.40[2], med.MAR.cov.70.AD.women.cl.25.40[2], 
                     med.MAR.cov.75.AD.women.cl.25.40[2], med.MAR.cov.80.AD.women.cl.25.40[2], 
                     med.MAR.cov.85.AD.women.cl.25.40[2], med.MAR.cov.90.AD.women.cl.25.40[2], 
                     med.MAR.cov.95.AD.women.cl.25.40[2], med.AD.num.women.true.cov.100.25.40[2],
                     
                     med.MAR.cov.35.AD.men.cl.25.40[2], med.MAR.cov.40.AD.men.cl.25.40[2], 
                     med.MAR.cov.45.AD.men.cl.25.40[2], med.MAR.cov.50.AD.men.cl.25.40[2], 
                     med.MAR.cov.55.AD.men.cl.25.40[2], med.MAR.cov.60.AD.men.cl.25.40[2], 
                     med.MAR.cov.65.AD.men.cl.25.40[2], med.MAR.cov.70.AD.men.cl.25.40[2], 
                     med.MAR.cov.75.AD.men.cl.25.40[2], med.MAR.cov.80.AD.men.cl.25.40[2], 
                     med.MAR.cov.85.AD.men.cl.25.40[2], med.MAR.cov.90.AD.men.cl.25.40[2], 
                     med.MAR.cov.95.AD.men.cl.25.40[2], med.AD.num.men.true.cov.100.25.40[2],
                     
                     med.MAR.cov.35.AD.women.cl.40.50[2], med.MAR.cov.40.AD.women.cl.40.50[2], 
                     med.MAR.cov.45.AD.women.cl.40.50[2], med.MAR.cov.50.AD.women.cl.40.50[2], 
                     med.MAR.cov.55.AD.women.cl.40.50[2], med.MAR.cov.60.AD.women.cl.40.50[2], 
                     med.MAR.cov.65.AD.women.cl.40.50[2], med.MAR.cov.70.AD.women.cl.40.50[2], 
                     med.MAR.cov.75.AD.women.cl.40.50[2], med.MAR.cov.80.AD.women.cl.40.50[2], 
                     med.MAR.cov.85.AD.women.cl.40.50[2], med.MAR.cov.90.AD.women.cl.40.50[2], 
                     med.MAR.cov.95.AD.women.cl.40.50[2], med.AD.num.women.true.cov.100.40.50[2],
                     
                     med.MAR.cov.35.AD.men.cl.40.50[2], med.MAR.cov.40.AD.men.cl.40.50[2], 
                     med.MAR.cov.45.AD.men.cl.40.50[2], med.MAR.cov.50.AD.men.cl.40.50[2], 
                     med.MAR.cov.55.AD.men.cl.40.50[2], med.MAR.cov.60.AD.men.cl.40.50[2], 
                     med.MAR.cov.65.AD.men.cl.40.50[2], med.MAR.cov.70.AD.men.cl.40.50[2], 
                     med.MAR.cov.75.AD.men.cl.40.50[2], med.MAR.cov.80.AD.men.cl.40.50[2], 
                     med.MAR.cov.85.AD.men.cl.40.50[2], med.MAR.cov.90.AD.men.cl.40.50[2], 
                     med.MAR.cov.95.AD.men.cl.40.50[2], med.AD.num.men.true.cov.100.40.50[2],
                     
                     sd.MAR.cov.35.AD.women.cl.15.25[2], sd.MAR.cov.40.AD.women.cl.15.25[2], 
                     sd.MAR.cov.45.AD.women.cl.15.25[2], sd.MAR.cov.50.AD.women.cl.15.25[2], 
                     sd.MAR.cov.55.AD.women.cl.15.25[2], sd.MAR.cov.60.AD.women.cl.15.25[2], 
                     sd.MAR.cov.65.AD.women.cl.15.25[2], sd.MAR.cov.70.AD.women.cl.15.25[2], 
                     sd.MAR.cov.75.AD.women.cl.15.25[2], sd.MAR.cov.80.AD.women.cl.15.25[2], 
                     sd.MAR.cov.85.AD.women.cl.15.25[2], sd.MAR.cov.90.AD.women.cl.15.25[2], 
                     sd.MAR.cov.95.AD.women.cl.15.25[2], sd.AD.num.women.true.cov.100.15.25[2],
                     
                     sd.MAR.cov.35.AD.men.cl.15.25[2], sd.MAR.cov.40.AD.men.cl.15.25[2], 
                     sd.MAR.cov.45.AD.men.cl.15.25[2], sd.MAR.cov.50.AD.men.cl.15.25[2], 
                     sd.MAR.cov.55.AD.men.cl.15.25[2], sd.MAR.cov.60.AD.men.cl.15.25[2], 
                     sd.MAR.cov.65.AD.men.cl.15.25[2], sd.MAR.cov.70.AD.men.cl.15.25[2], 
                     sd.MAR.cov.75.AD.men.cl.15.25[2], sd.MAR.cov.80.AD.men.cl.15.25[2], 
                     sd.MAR.cov.85.AD.men.cl.15.25[2], sd.MAR.cov.90.AD.men.cl.15.25[2], 
                     sd.MAR.cov.95.AD.men.cl.15.25[2], sd.AD.num.men.true.cov.100.15.25[2],
                     
                     sd.MAR.cov.35.AD.women.cl.25.40[2], sd.MAR.cov.40.AD.women.cl.25.40[2], 
                     sd.MAR.cov.45.AD.women.cl.25.40[2], sd.MAR.cov.50.AD.women.cl.25.40[2], 
                     sd.MAR.cov.55.AD.women.cl.25.40[2], sd.MAR.cov.60.AD.women.cl.25.40[2], 
                     sd.MAR.cov.65.AD.women.cl.25.40[2], sd.MAR.cov.70.AD.women.cl.25.40[2], 
                     sd.MAR.cov.75.AD.women.cl.25.40[2], sd.MAR.cov.80.AD.women.cl.25.40[2], 
                     sd.MAR.cov.85.AD.women.cl.25.40[2], sd.MAR.cov.90.AD.women.cl.25.40[2], 
                     sd.MAR.cov.95.AD.women.cl.25.40[2], sd.AD.num.women.true.cov.100.25.40[2],
                     
                     sd.MAR.cov.35.AD.men.cl.25.40[2], sd.MAR.cov.40.AD.men.cl.25.40[2], 
                     sd.MAR.cov.45.AD.men.cl.25.40[2], sd.MAR.cov.50.AD.men.cl.25.40[2], 
                     sd.MAR.cov.55.AD.men.cl.25.40[2], sd.MAR.cov.60.AD.men.cl.25.40[2], 
                     sd.MAR.cov.65.AD.men.cl.25.40[2], sd.MAR.cov.70.AD.men.cl.25.40[2], 
                     sd.MAR.cov.75.AD.men.cl.25.40[2], sd.MAR.cov.80.AD.men.cl.25.40[2], 
                     sd.MAR.cov.85.AD.men.cl.25.40[2], sd.MAR.cov.90.AD.men.cl.25.40[2], 
                     sd.MAR.cov.95.AD.men.cl.25.40[2], sd.AD.num.men.true.cov.100.25.40[2],
                     
                     sd.MAR.cov.35.AD.women.cl.40.50[2], sd.MAR.cov.40.AD.women.cl.40.50[2], 
                     sd.MAR.cov.45.AD.women.cl.40.50[2], sd.MAR.cov.50.AD.women.cl.40.50[2], 
                     sd.MAR.cov.55.AD.women.cl.40.50[2], sd.MAR.cov.60.AD.women.cl.40.50[2], 
                     sd.MAR.cov.65.AD.women.cl.40.50[2], sd.MAR.cov.70.AD.women.cl.40.50[2], 
                     sd.MAR.cov.75.AD.women.cl.40.50[2], sd.MAR.cov.80.AD.women.cl.40.50[2], 
                     sd.MAR.cov.85.AD.women.cl.40.50[2], sd.MAR.cov.90.AD.women.cl.40.50[2], 
                     sd.MAR.cov.95.AD.women.cl.40.50[2], sd.AD.num.women.true.cov.100.40.50[2],
                     
                     sd.MAR.cov.35.AD.men.cl.40.50[2], sd.MAR.cov.40.AD.men.cl.40.50[2], 
                     sd.MAR.cov.45.AD.men.cl.40.50[2], sd.MAR.cov.50.AD.men.cl.40.50[2], 
                     sd.MAR.cov.55.AD.men.cl.40.50[2], sd.MAR.cov.60.AD.men.cl.40.50[2], 
                     sd.MAR.cov.65.AD.men.cl.40.50[2], sd.MAR.cov.70.AD.men.cl.40.50[2], 
                     sd.MAR.cov.75.AD.men.cl.40.50[2], sd.MAR.cov.80.AD.men.cl.40.50[2], 
                     sd.MAR.cov.85.AD.men.cl.40.50[2], sd.MAR.cov.90.AD.men.cl.40.50[2], 
                     sd.MAR.cov.95.AD.men.cl.40.50[2], sd.AD.num.men.true.cov.100.40.50[2]
                     
                     
                     
),

ncol = 14,
byrow = TRUE)

AD.stats <- round(AD.stats, digits = 2)

colnames(AD.stats) <- c("35", "40", "45",
                        "50", "55", "60",
                        "65", "70", "75",
                        "80", "85", "90",
                        "95", "true_100")

rownames(AD.stats) <- c("mean.AD.women.cl.15.25", "mean.AD.men.cl.15.25", 
                        "mean.AD.women.cl.25.40", "mean.AD.men.cl.25.40", 
                        "mean.AD.women.cl.40.50", "mean.AD.men.cl.40.50",
                        
                        "med.AD.women.cl.15.25", "med.AD.men.cl.15.25",
                        "med.AD.women.cl.25.40", "med.AD.men.cl.25.40",
                        "med.AD.women.cl.40.50", "med.AD.men.cl.40.50",
                        
                        "sd.AD.women.cl.15.25", "sd.AD.men.cl.15.25",
                        "sd.AD.women.cl.25.40", "sd.AD.men.cl.25.40",
                        "sd.AD.women.cl.40.50", "sd.AD.men.cl.40.50") 


AD.stats %>% 
  kable() %>% 
  kable_styling("striped")
35 40 45 50 55 60 65 70 75 80 85 90 95 true_100
mean.AD.women.cl.15.25 11.89 11.84 12.09 11.80 11.75 11.67 11.88 11.96 12.13 12.06 12.10 12.17 12.26 13.21
mean.AD.men.cl.15.25 2.75 2.75 2.70 2.88 2.93 2.94 2.96 2.91 2.97 3.03 2.89 3.02 2.99 2.91
mean.AD.women.cl.25.40 15.10 15.07 14.82 14.86 14.69 14.96 14.77 14.57 14.64 14.85 14.87 14.65 14.55 13.70
mean.AD.men.cl.25.40 10.94 10.84 10.92 10.61 10.61 10.89 10.78 10.83 10.86 10.95 10.86 11.18 10.97 11.46
mean.AD.women.cl.40.50 4.56 3.83 6.97 4.73 6.57 4.90 4.31 4.82 4.66 6.80 7.46 5.11 5.93 4.65
mean.AD.men.cl.40.50 21.68 20.51 21.23 21.02 20.66 20.61 20.67 20.51 20.88 20.86 20.71 20.81 21.01 20.35
med.AD.women.cl.15.25 11.57 11.70 12.07 11.44 11.57 11.53 11.55 11.78 11.91 11.86 12.21 12.15 11.99 13.06
med.AD.men.cl.15.25 2.67 2.70 2.59 2.68 2.73 2.75 2.81 2.75 2.74 2.81 2.56 2.79 2.79 2.72
med.AD.women.cl.25.40 15.23 15.20 14.98 15.12 14.91 15.19 15.21 15.01 15.19 15.20 15.32 15.11 14.92 14.57
med.AD.men.cl.25.40 11.18 10.90 11.01 10.58 10.74 10.93 10.76 10.84 10.90 10.87 10.89 11.25 11.00 11.44
med.AD.women.cl.40.50 4.56 3.83 6.88 5.99 6.57 3.40 4.62 4.82 4.75 7.12 7.46 5.32 5.93 4.35
med.AD.men.cl.40.50 21.78 20.74 21.41 21.01 20.88 20.70 20.77 20.65 20.88 21.14 20.63 20.92 21.07 20.49
sd.AD.women.cl.15.25 5.70 5.92 5.97 6.25 6.37 6.39 6.27 6.45 6.48 6.68 6.61 6.59 6.68 6.92
sd.AD.men.cl.15.25 1.59 1.73 1.66 1.78 1.82 1.87 1.80 1.79 1.84 1.88 1.88 1.92 1.90 1.88
sd.AD.women.cl.25.40 3.16 3.35 3.96 3.60 3.61 3.68 4.13 4.01 3.75 4.07 4.37 4.18 4.11 5.03
sd.AD.men.cl.25.40 2.99 3.08 3.50 3.29 3.60 3.66 3.72 3.75 3.82 3.93 3.84 3.80 4.03 4.18
sd.AD.women.cl.40.50 1.16 1.65 6.37 2.69 4.05 2.55 2.97 3.57 2.60 3.19 4.47 4.65 2.83 2.53
sd.AD.men.cl.40.50 2.66 2.61 2.30 2.48 2.39 2.59 2.47 2.86 2.72 2.88 2.89 2.85 3.08 3.76
write.csv(AD.stats, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Table_a_16_Age_Difference_Stats_at_35_95_Coverage.csv")

There is no much descrepensies between values of statistics (mean, median, and standard deviation) of true average age differences and these inferred from transmission clusters. And it seems that sequence coverage does not has an direct effect of these statistics, they look like they are stable across all sequence coverage scenarios.

One important aspect which elucidates age mixing in transmissison clusters from age difference inferred from pairings built from phylogenetic tree is: the average age difference of women between 15 and 25 years age group (~13 years with 6 years of deviation) and men between 40 and 50 years age group (~20 years with 3 years of deviation), besides for men and women in 25 and 40 years age groups (~ 14 years for women and 11 years for men with a deviation of 3-4 years for both).

This prove the existance of age mixing patterns across the transmission networks which we have seen in the true record of partnership.

# mean.women.15.25-------------------- 

mean.AD.women.15.25.df <- data.frame(x=c("35", "40", "45",
                                         "50", "55", "60",
                                         "65", "70", "75",
                                         "80", "85", "90",
                                         "95", "true_100"),
                                     
                                     F = c(mean.MAR.cov.35.AD.women.cl.15.25[2], mean.MAR.cov.40.AD.women.cl.15.25[2], 
                                           mean.MAR.cov.45.AD.women.cl.15.25[2], mean.MAR.cov.50.AD.women.cl.15.25[2], 
                                           mean.MAR.cov.55.AD.women.cl.15.25[2], mean.MAR.cov.60.AD.women.cl.15.25[2], 
                                           mean.MAR.cov.65.AD.women.cl.15.25[2], mean.MAR.cov.70.AD.women.cl.15.25[2], 
                                           mean.MAR.cov.75.AD.women.cl.15.25[2], mean.MAR.cov.80.AD.women.cl.15.25[2], 
                                           mean.MAR.cov.85.AD.women.cl.15.25[2], mean.MAR.cov.90.AD.women.cl.15.25[2], 
                                           mean.MAR.cov.95.AD.women.cl.15.25[2], mean.AD.num.women.true.cov.100.15.25[2]),
                                     
                                     L = c(mean.MAR.cov.35.AD.women.cl.15.25[1], mean.MAR.cov.40.AD.women.cl.15.25[1], 
                                           mean.MAR.cov.45.AD.women.cl.15.25[1], mean.MAR.cov.50.AD.women.cl.15.25[1], 
                                           mean.MAR.cov.55.AD.women.cl.15.25[1], mean.MAR.cov.60.AD.women.cl.15.25[1], 
                                           mean.MAR.cov.65.AD.women.cl.15.25[1], mean.MAR.cov.70.AD.women.cl.15.25[1], 
                                           mean.MAR.cov.75.AD.women.cl.15.25[1], mean.MAR.cov.80.AD.women.cl.15.25[1], 
                                           mean.MAR.cov.85.AD.women.cl.15.25[1], mean.MAR.cov.90.AD.women.cl.15.25[1], 
                                           mean.MAR.cov.95.AD.women.cl.15.25[1], mean.AD.num.women.true.cov.100.15.25[1]),
                                     
                                     U = c(mean.MAR.cov.35.AD.women.cl.15.25[3], mean.MAR.cov.40.AD.women.cl.15.25[3], 
                                           mean.MAR.cov.45.AD.women.cl.15.25[3], mean.MAR.cov.50.AD.women.cl.15.25[3], 
                                           mean.MAR.cov.55.AD.women.cl.15.25[3], mean.MAR.cov.60.AD.women.cl.15.25[3], 
                                           mean.MAR.cov.65.AD.women.cl.15.25[3], mean.MAR.cov.70.AD.women.cl.15.25[3], 
                                           mean.MAR.cov.75.AD.women.cl.15.25[3], mean.MAR.cov.80.AD.women.cl.15.25[3], 
                                           mean.MAR.cov.85.AD.women.cl.15.25[3], mean.MAR.cov.90.AD.women.cl.15.25[3], 
                                           mean.MAR.cov.95.AD.women.cl.15.25[3], mean.AD.num.women.true.cov.100.15.25[3]))

mean.AD.women.15.25.df$parameter <- rep("mean.AD.women.15.25", nrow(mean.AD.women.15.25.df))




# mean.men.15.25-------------------- 

mean.AD.men.15.25.df <- data.frame(x=c("35", "40", "45",
                                       "50", "55", "60",
                                       "65", "70", "75",
                                       "80", "85", "90",
                                       "95", "true_100"),
                                   
                                   F = c(mean.MAR.cov.35.AD.men.cl.15.25[2], mean.MAR.cov.40.AD.men.cl.15.25[2], 
                                         mean.MAR.cov.45.AD.men.cl.15.25[2], mean.MAR.cov.50.AD.men.cl.15.25[2], 
                                         mean.MAR.cov.55.AD.men.cl.15.25[2], mean.MAR.cov.60.AD.men.cl.15.25[2], 
                                         mean.MAR.cov.65.AD.men.cl.15.25[2], mean.MAR.cov.70.AD.men.cl.15.25[2], 
                                         mean.MAR.cov.75.AD.men.cl.15.25[2], mean.MAR.cov.80.AD.men.cl.15.25[2], 
                                         mean.MAR.cov.85.AD.men.cl.15.25[2], mean.MAR.cov.90.AD.men.cl.15.25[2], 
                                         mean.MAR.cov.95.AD.men.cl.15.25[2], mean.AD.num.men.true.cov.100.15.25[2]),
                                   
                                   L = c(mean.MAR.cov.35.AD.men.cl.15.25[1], mean.MAR.cov.40.AD.men.cl.15.25[1], 
                                         mean.MAR.cov.45.AD.men.cl.15.25[1], mean.MAR.cov.50.AD.men.cl.15.25[1], 
                                         mean.MAR.cov.55.AD.men.cl.15.25[1], mean.MAR.cov.60.AD.men.cl.15.25[1], 
                                         mean.MAR.cov.65.AD.men.cl.15.25[1], mean.MAR.cov.70.AD.men.cl.15.25[1], 
                                         mean.MAR.cov.75.AD.men.cl.15.25[1], mean.MAR.cov.80.AD.men.cl.15.25[1], 
                                         mean.MAR.cov.85.AD.men.cl.15.25[1], mean.MAR.cov.90.AD.men.cl.15.25[1], 
                                         mean.MAR.cov.95.AD.men.cl.15.25[1], mean.AD.num.men.true.cov.100.15.25[1]),
                                   
                                   U = c(mean.MAR.cov.35.AD.men.cl.15.25[3], mean.MAR.cov.40.AD.men.cl.15.25[3], 
                                         mean.MAR.cov.45.AD.men.cl.15.25[3], mean.MAR.cov.50.AD.men.cl.15.25[3], 
                                         mean.MAR.cov.55.AD.men.cl.15.25[3], mean.MAR.cov.60.AD.men.cl.15.25[3], 
                                         mean.MAR.cov.65.AD.men.cl.15.25[3], mean.MAR.cov.70.AD.men.cl.15.25[3], 
                                         mean.MAR.cov.75.AD.men.cl.15.25[3], mean.MAR.cov.80.AD.men.cl.15.25[3], 
                                         mean.MAR.cov.85.AD.men.cl.15.25[3], mean.MAR.cov.90.AD.men.cl.15.25[3], 
                                         mean.MAR.cov.95.AD.men.cl.15.25[3], mean.AD.num.men.true.cov.100.15.25[3]))



mean.AD.men.15.25.df$parameter <- rep("mean.AD.men.15.25", nrow(mean.AD.men.15.25.df))


# mean.women.25.40-------------------- 

mean.AD.women.25.40.df <- data.frame(x=c("35", "40", "45",
                                         "50", "55", "60",
                                         "65", "70", "75",
                                         "80", "85", "90",
                                         "95", "true_100"),
                                     
                                     F = c(mean.MAR.cov.35.AD.women.cl.25.40[2], mean.MAR.cov.40.AD.women.cl.25.40[2], 
                                           mean.MAR.cov.45.AD.women.cl.25.40[2], mean.MAR.cov.50.AD.women.cl.25.40[2], 
                                           mean.MAR.cov.55.AD.women.cl.25.40[2], mean.MAR.cov.60.AD.women.cl.25.40[2], 
                                           mean.MAR.cov.65.AD.women.cl.25.40[2], mean.MAR.cov.70.AD.women.cl.25.40[2], 
                                           mean.MAR.cov.75.AD.women.cl.25.40[2], mean.MAR.cov.80.AD.women.cl.25.40[2], 
                                           mean.MAR.cov.85.AD.women.cl.25.40[2], mean.MAR.cov.90.AD.women.cl.25.40[2], 
                                           mean.MAR.cov.95.AD.women.cl.25.40[2], mean.AD.num.women.true.cov.100.25.40[2]),
                                     
                                     L = c(mean.MAR.cov.35.AD.women.cl.25.40[1], mean.MAR.cov.40.AD.women.cl.25.40[1], 
                                           mean.MAR.cov.45.AD.women.cl.25.40[1], mean.MAR.cov.50.AD.women.cl.25.40[1], 
                                           mean.MAR.cov.55.AD.women.cl.25.40[1], mean.MAR.cov.60.AD.women.cl.25.40[1], 
                                           mean.MAR.cov.65.AD.women.cl.25.40[1], mean.MAR.cov.70.AD.women.cl.25.40[1], 
                                           mean.MAR.cov.75.AD.women.cl.25.40[1], mean.MAR.cov.80.AD.women.cl.25.40[1], 
                                           mean.MAR.cov.85.AD.women.cl.25.40[1], mean.MAR.cov.90.AD.women.cl.25.40[1], 
                                           mean.MAR.cov.95.AD.women.cl.25.40[1], mean.AD.num.women.true.cov.100.25.40[1]),
                                     
                                     U = c(mean.MAR.cov.35.AD.women.cl.25.40[3], mean.MAR.cov.40.AD.women.cl.25.40[3], 
                                           mean.MAR.cov.45.AD.women.cl.25.40[3], mean.MAR.cov.50.AD.women.cl.25.40[3], 
                                           mean.MAR.cov.55.AD.women.cl.25.40[3], mean.MAR.cov.60.AD.women.cl.25.40[3], 
                                           mean.MAR.cov.65.AD.women.cl.25.40[3], mean.MAR.cov.70.AD.women.cl.25.40[3], 
                                           mean.MAR.cov.75.AD.women.cl.25.40[3], mean.MAR.cov.80.AD.women.cl.25.40[3], 
                                           mean.MAR.cov.85.AD.women.cl.25.40[3], mean.MAR.cov.90.AD.women.cl.25.40[3], 
                                           mean.MAR.cov.95.AD.women.cl.25.40[3], mean.AD.num.women.true.cov.100.25.40[3]))



mean.AD.women.25.40.df$parameter <- rep("mean.AD.women.25.40", nrow(mean.AD.women.25.40.df))





# mean.men.25.40-------------------- 

mean.AD.men.25.40.df <- data.frame(x=c("35", "40", "45",
                                       "50", "55", "60",
                                       "65", "70", "75",
                                       "80", "85", "90",
                                       "95", "true_100"),
                                   
                                   F = c(mean.MAR.cov.35.AD.men.cl.25.40[2], mean.MAR.cov.40.AD.men.cl.25.40[2], 
                                         mean.MAR.cov.45.AD.men.cl.25.40[2], mean.MAR.cov.50.AD.men.cl.25.40[2], 
                                         mean.MAR.cov.55.AD.men.cl.25.40[2], mean.MAR.cov.60.AD.men.cl.25.40[2], 
                                         mean.MAR.cov.65.AD.men.cl.25.40[2], mean.MAR.cov.70.AD.men.cl.25.40[2], 
                                         mean.MAR.cov.75.AD.men.cl.25.40[2], mean.MAR.cov.80.AD.men.cl.25.40[2], 
                                         mean.MAR.cov.85.AD.men.cl.25.40[2], mean.MAR.cov.90.AD.men.cl.25.40[2], 
                                         mean.MAR.cov.95.AD.men.cl.25.40[2], mean.AD.num.men.true.cov.100.25.40[2]),
                                   
                                   L = c(mean.MAR.cov.35.AD.men.cl.25.40[1], mean.MAR.cov.40.AD.men.cl.25.40[1], 
                                         mean.MAR.cov.45.AD.men.cl.25.40[1], mean.MAR.cov.50.AD.men.cl.25.40[1], 
                                         mean.MAR.cov.55.AD.men.cl.25.40[1], mean.MAR.cov.60.AD.men.cl.25.40[1], 
                                         mean.MAR.cov.65.AD.men.cl.25.40[1], mean.MAR.cov.70.AD.men.cl.25.40[1], 
                                         mean.MAR.cov.75.AD.men.cl.25.40[1], mean.MAR.cov.80.AD.men.cl.25.40[1], 
                                         mean.MAR.cov.85.AD.men.cl.25.40[1], mean.MAR.cov.90.AD.men.cl.25.40[1], 
                                         mean.MAR.cov.95.AD.men.cl.25.40[1], mean.AD.num.men.true.cov.100.25.40[1]),
                                   
                                   U = c(mean.MAR.cov.35.AD.men.cl.25.40[3], mean.MAR.cov.40.AD.men.cl.25.40[3], 
                                         mean.MAR.cov.45.AD.men.cl.25.40[3], mean.MAR.cov.50.AD.men.cl.25.40[3], 
                                         mean.MAR.cov.55.AD.men.cl.25.40[3], mean.MAR.cov.60.AD.men.cl.25.40[3], 
                                         mean.MAR.cov.65.AD.men.cl.25.40[3], mean.MAR.cov.70.AD.men.cl.25.40[3], 
                                         mean.MAR.cov.75.AD.men.cl.25.40[3], mean.MAR.cov.80.AD.men.cl.25.40[3], 
                                         mean.MAR.cov.85.AD.men.cl.25.40[3], mean.MAR.cov.90.AD.men.cl.25.40[3], 
                                         mean.MAR.cov.95.AD.men.cl.25.40[3], mean.AD.num.men.true.cov.100.25.40[3]))



mean.AD.men.25.40.df$parameter <- rep("mean.AD.men.25.40", nrow(mean.AD.men.25.40.df))





# mean.women.40.50-------------------- 

mean.AD.women.40.50.df <- data.frame(x=c("35", "40", "45",
                                         "50", "55", "60",
                                         "65", "70", "75",
                                         "80", "85", "90",
                                         "95", "true_100"),
                                     
                                     F = c(mean.MAR.cov.35.AD.women.cl.40.50[2], mean.MAR.cov.40.AD.women.cl.40.50[2], 
                                           mean.MAR.cov.45.AD.women.cl.40.50[2], mean.MAR.cov.50.AD.women.cl.40.50[2], 
                                           mean.MAR.cov.55.AD.women.cl.40.50[2], mean.MAR.cov.60.AD.women.cl.40.50[2], 
                                           mean.MAR.cov.65.AD.women.cl.40.50[2], mean.MAR.cov.70.AD.women.cl.40.50[2], 
                                           mean.MAR.cov.75.AD.women.cl.40.50[2], mean.MAR.cov.80.AD.women.cl.40.50[2], 
                                           mean.MAR.cov.85.AD.women.cl.40.50[2], mean.MAR.cov.90.AD.women.cl.40.50[2], 
                                           mean.MAR.cov.95.AD.women.cl.40.50[2], mean.AD.num.women.true.cov.100.40.50[2]),
                                     
                                     L = c(mean.MAR.cov.35.AD.women.cl.40.50[1], mean.MAR.cov.40.AD.women.cl.40.50[1], 
                                           mean.MAR.cov.45.AD.women.cl.40.50[1], mean.MAR.cov.50.AD.women.cl.40.50[1], 
                                           mean.MAR.cov.55.AD.women.cl.40.50[1], mean.MAR.cov.60.AD.women.cl.40.50[1], 
                                           mean.MAR.cov.65.AD.women.cl.40.50[1], mean.MAR.cov.70.AD.women.cl.40.50[1], 
                                           mean.MAR.cov.75.AD.women.cl.40.50[1], mean.MAR.cov.80.AD.women.cl.40.50[1], 
                                           mean.MAR.cov.85.AD.women.cl.40.50[1], mean.MAR.cov.90.AD.women.cl.40.50[1], 
                                           mean.MAR.cov.95.AD.women.cl.40.50[1], mean.AD.num.women.true.cov.100.40.50[1]),
                                     
                                     U = c(mean.MAR.cov.35.AD.women.cl.40.50[3], mean.MAR.cov.40.AD.women.cl.40.50[3], 
                                           mean.MAR.cov.45.AD.women.cl.40.50[3], mean.MAR.cov.50.AD.women.cl.40.50[3], 
                                           mean.MAR.cov.55.AD.women.cl.40.50[3], mean.MAR.cov.60.AD.women.cl.40.50[3], 
                                           mean.MAR.cov.65.AD.women.cl.40.50[3], mean.MAR.cov.70.AD.women.cl.40.50[3], 
                                           mean.MAR.cov.75.AD.women.cl.40.50[3], mean.MAR.cov.80.AD.women.cl.40.50[3], 
                                           mean.MAR.cov.85.AD.women.cl.40.50[3], mean.MAR.cov.90.AD.women.cl.40.50[3], 
                                           mean.MAR.cov.95.AD.women.cl.40.50[3], mean.AD.num.women.true.cov.100.40.50[3]))

mean.AD.women.40.50.df$parameter <- rep("mean.AD.women.40.50", nrow(mean.AD.women.40.50.df))




# mean.men.40.50-------------------- 

mean.AD.men.40.50.df <- data.frame(x=c("35", "40", "45",
                                       "50", "55", "60",
                                       "65", "70", "75",
                                       "80", "85", "90",
                                       "95", "true_100"),
                                   
                                   F = c(mean.MAR.cov.35.AD.men.cl.40.50[2], mean.MAR.cov.40.AD.men.cl.40.50[2], 
                                         mean.MAR.cov.45.AD.men.cl.40.50[2], mean.MAR.cov.50.AD.men.cl.40.50[2], 
                                         mean.MAR.cov.55.AD.men.cl.40.50[2], mean.MAR.cov.60.AD.men.cl.40.50[2], 
                                         mean.MAR.cov.65.AD.men.cl.40.50[2], mean.MAR.cov.70.AD.men.cl.40.50[2], 
                                         mean.MAR.cov.75.AD.men.cl.40.50[2], mean.MAR.cov.80.AD.men.cl.40.50[2], 
                                         mean.MAR.cov.85.AD.men.cl.40.50[2], mean.MAR.cov.90.AD.men.cl.40.50[2], 
                                         mean.MAR.cov.95.AD.men.cl.40.50[2], mean.AD.num.men.true.cov.100.40.50[2]),
                                   
                                   L = c(mean.MAR.cov.35.AD.men.cl.40.50[1], mean.MAR.cov.40.AD.men.cl.40.50[1], 
                                         mean.MAR.cov.45.AD.men.cl.40.50[1], mean.MAR.cov.50.AD.men.cl.40.50[1], 
                                         mean.MAR.cov.55.AD.men.cl.40.50[1], mean.MAR.cov.60.AD.men.cl.40.50[1], 
                                         mean.MAR.cov.65.AD.men.cl.40.50[1], mean.MAR.cov.70.AD.men.cl.40.50[1], 
                                         mean.MAR.cov.75.AD.men.cl.40.50[1], mean.MAR.cov.80.AD.men.cl.40.50[1], 
                                         mean.MAR.cov.85.AD.men.cl.40.50[1], mean.MAR.cov.90.AD.men.cl.40.50[1], 
                                         mean.MAR.cov.95.AD.men.cl.40.50[1], mean.AD.num.men.true.cov.100.40.50[1]),
                                   
                                   U = c(mean.MAR.cov.35.AD.men.cl.40.50[3], mean.MAR.cov.40.AD.men.cl.40.50[3], 
                                         mean.MAR.cov.45.AD.men.cl.40.50[3], mean.MAR.cov.50.AD.men.cl.40.50[3], 
                                         mean.MAR.cov.55.AD.men.cl.40.50[3], mean.MAR.cov.60.AD.men.cl.40.50[3], 
                                         mean.MAR.cov.65.AD.men.cl.40.50[3], mean.MAR.cov.70.AD.men.cl.40.50[3], 
                                         mean.MAR.cov.75.AD.men.cl.40.50[3], mean.MAR.cov.80.AD.men.cl.40.50[3], 
                                         mean.MAR.cov.85.AD.men.cl.40.50[3], mean.MAR.cov.90.AD.men.cl.40.50[3], 
                                         mean.MAR.cov.95.AD.men.cl.40.50[3], mean.AD.num.men.true.cov.100.40.50[3]))



mean.AD.men.40.50.df$parameter <- rep("mean.AD.men.40.50", nrow(mean.AD.men.40.50.df))



mean.men.AD.df <- rbind(mean.AD.men.15.25.df,
                        mean.AD.men.25.40.df,
                        mean.AD.men.40.50.df)

mean.men.AD.df$gender <- rep("Males", nrow(mean.men.AD.df))

mean.women.AD.df <- rbind(mean.AD.women.15.25.df,
                          mean.AD.women.25.40.df,
                          mean.AD.women.40.50.df)

mean.women.AD.df$gender <- rep("Females", nrow(mean.women.AD.df))

df_all <- rbind(mean.men.AD.df, mean.women.AD.df)

colnames(df_all) <- c("cov", "F", "L", "U", "Parameter", "f_m")

df_all$age_grps <- c(rep("15_24", 14), rep("25_39", 14), rep("40_49", 14), rep("15_24", 14), rep("25_39", 14), rep("40_49", 14))


saveRDS(df_all, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/MAR_a_11_Mean_Age_Difference_at_35_95_Coverage.RDS")


plot.mean.men.women.AD.df <- ggplot(df_all, aes(x=cov, y=F, colour=age_grps, group=Parameter)) + 
  geom_line(size=1) +
  geom_point() +
  facet_grid(.~f_m)+
  # geom_errorbar(aes(ymin=L, ymax=U), width=.1) +
  # ggtitle("Mean age difference in MCAR") +
  xlab("Sampling Coverage (%)") + ylab("Mean Age Difference") # +


print(plot.mean.men.women.AD.df)

ggsave(filename = "Plot_a_11_Mean_Age_Difference_at_35_95_Coverage.pdf",
       plot = plot.mean.men.women.AD.df,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 30, height = 15, units = "cm")
# med.women.15.25-------------------- 

med.AD.women.15.25.df <- data.frame(x=c("35", "40", "45",
                                        "50", "55", "60",
                                        "65", "70", "75",
                                        "80", "85", "90",
                                        "95", "true_100"),
                                    
                                    F = c(med.MAR.cov.35.AD.women.cl.15.25[2], med.MAR.cov.40.AD.women.cl.15.25[2], 
                                          med.MAR.cov.45.AD.women.cl.15.25[2], med.MAR.cov.50.AD.women.cl.15.25[2], 
                                          med.MAR.cov.55.AD.women.cl.15.25[2], med.MAR.cov.60.AD.women.cl.15.25[2], 
                                          med.MAR.cov.65.AD.women.cl.15.25[2], med.MAR.cov.70.AD.women.cl.15.25[2], 
                                          med.MAR.cov.75.AD.women.cl.15.25[2], med.MAR.cov.80.AD.women.cl.15.25[2], 
                                          med.MAR.cov.85.AD.women.cl.15.25[2], med.MAR.cov.90.AD.women.cl.15.25[2], 
                                          med.MAR.cov.95.AD.women.cl.15.25[2], med.AD.num.women.true.cov.100.15.25[2]),
                                    
                                    L = c(med.MAR.cov.35.AD.women.cl.15.25[1], med.MAR.cov.40.AD.women.cl.15.25[1], 
                                          med.MAR.cov.45.AD.women.cl.15.25[1], med.MAR.cov.50.AD.women.cl.15.25[1], 
                                          med.MAR.cov.55.AD.women.cl.15.25[1], med.MAR.cov.60.AD.women.cl.15.25[1], 
                                          med.MAR.cov.65.AD.women.cl.15.25[1], med.MAR.cov.70.AD.women.cl.15.25[1], 
                                          med.MAR.cov.75.AD.women.cl.15.25[1], med.MAR.cov.80.AD.women.cl.15.25[1], 
                                          med.MAR.cov.85.AD.women.cl.15.25[1], med.MAR.cov.90.AD.women.cl.15.25[1], 
                                          med.MAR.cov.95.AD.women.cl.15.25[1], med.AD.num.women.true.cov.100.15.25[1]),
                                    
                                    U = c(med.MAR.cov.35.AD.women.cl.15.25[3], med.MAR.cov.40.AD.women.cl.15.25[3], 
                                          med.MAR.cov.45.AD.women.cl.15.25[3], med.MAR.cov.50.AD.women.cl.15.25[3], 
                                          med.MAR.cov.55.AD.women.cl.15.25[3], med.MAR.cov.60.AD.women.cl.15.25[3], 
                                          med.MAR.cov.65.AD.women.cl.15.25[3], med.MAR.cov.70.AD.women.cl.15.25[3], 
                                          med.MAR.cov.75.AD.women.cl.15.25[3], med.MAR.cov.80.AD.women.cl.15.25[3], 
                                          med.MAR.cov.85.AD.women.cl.15.25[3], med.MAR.cov.90.AD.women.cl.15.25[3], 
                                          med.MAR.cov.95.AD.women.cl.15.25[3], med.AD.num.women.true.cov.100.15.25[3]))

med.AD.women.15.25.df$parameter <- rep("med.AD.women.15.25", nrow(med.AD.women.15.25.df)) 




# med.men.15.25-------------------- 

med.AD.men.15.25.df <- data.frame(x=c("35", "40", "45",
                                      "50", "55", "60",
                                      "65", "70", "75",
                                      "80", "85", "90",
                                      "95", "true_100"),
                                  
                                  F = c(med.MAR.cov.35.AD.men.cl.15.25[2], med.MAR.cov.40.AD.men.cl.15.25[2], 
                                        med.MAR.cov.45.AD.men.cl.15.25[2], med.MAR.cov.50.AD.men.cl.15.25[2], 
                                        med.MAR.cov.55.AD.men.cl.15.25[2], med.MAR.cov.60.AD.men.cl.15.25[2], 
                                        med.MAR.cov.65.AD.men.cl.15.25[2], med.MAR.cov.70.AD.men.cl.15.25[2], 
                                        med.MAR.cov.75.AD.men.cl.15.25[2], med.MAR.cov.80.AD.men.cl.15.25[2], 
                                        med.MAR.cov.85.AD.men.cl.15.25[2], med.MAR.cov.90.AD.men.cl.15.25[2], 
                                        med.MAR.cov.95.AD.men.cl.15.25[2], med.AD.num.men.true.cov.100.15.25[2]),
                                  
                                  L = c(med.MAR.cov.35.AD.men.cl.15.25[1], med.MAR.cov.40.AD.men.cl.15.25[1], 
                                        med.MAR.cov.45.AD.men.cl.15.25[1], med.MAR.cov.50.AD.men.cl.15.25[1], 
                                        med.MAR.cov.55.AD.men.cl.15.25[1], med.MAR.cov.60.AD.men.cl.15.25[1], 
                                        med.MAR.cov.65.AD.men.cl.15.25[1], med.MAR.cov.70.AD.men.cl.15.25[1], 
                                        med.MAR.cov.75.AD.men.cl.15.25[1], med.MAR.cov.80.AD.men.cl.15.25[1], 
                                        med.MAR.cov.85.AD.men.cl.15.25[1], med.MAR.cov.90.AD.men.cl.15.25[1], 
                                        med.MAR.cov.95.AD.men.cl.15.25[1], med.AD.num.men.true.cov.100.15.25[1]),
                                  
                                  U = c(med.MAR.cov.35.AD.men.cl.15.25[3], med.MAR.cov.40.AD.men.cl.15.25[3], 
                                        med.MAR.cov.45.AD.men.cl.15.25[3], med.MAR.cov.50.AD.men.cl.15.25[3], 
                                        med.MAR.cov.55.AD.men.cl.15.25[3], med.MAR.cov.60.AD.men.cl.15.25[3], 
                                        med.MAR.cov.65.AD.men.cl.15.25[3], med.MAR.cov.70.AD.men.cl.15.25[3], 
                                        med.MAR.cov.75.AD.men.cl.15.25[3], med.MAR.cov.80.AD.men.cl.15.25[3], 
                                        med.MAR.cov.85.AD.men.cl.15.25[3], med.MAR.cov.90.AD.men.cl.15.25[3], 
                                        med.MAR.cov.95.AD.men.cl.15.25[3], med.AD.num.men.true.cov.100.15.25[3]))


med.AD.men.15.25.df$parameter <- rep("med.AD.men.15.25", nrow(med.AD.men.15.25.df)) 




# med.women.25.40-------------------- 

med.AD.women.25.40.df <- data.frame(x=c("35", "40", "45",
                                        "50", "55", "60",
                                        "65", "70", "75",
                                        "80", "85", "90",
                                        "95", "true_100"),
                                    
                                    F = c(med.MAR.cov.35.AD.women.cl.25.40[2], med.MAR.cov.40.AD.women.cl.25.40[2], 
                                          med.MAR.cov.45.AD.women.cl.25.40[2], med.MAR.cov.50.AD.women.cl.25.40[2], 
                                          med.MAR.cov.55.AD.women.cl.25.40[2], med.MAR.cov.60.AD.women.cl.25.40[2], 
                                          med.MAR.cov.65.AD.women.cl.25.40[2], med.MAR.cov.70.AD.women.cl.25.40[2], 
                                          med.MAR.cov.75.AD.women.cl.25.40[2], med.MAR.cov.80.AD.women.cl.25.40[2], 
                                          med.MAR.cov.85.AD.women.cl.25.40[2], med.MAR.cov.90.AD.women.cl.25.40[2], 
                                          med.MAR.cov.95.AD.women.cl.25.40[2], med.AD.num.women.true.cov.100.25.40[2]),
                                    
                                    L = c(med.MAR.cov.35.AD.women.cl.25.40[1], med.MAR.cov.40.AD.women.cl.25.40[1], 
                                          med.MAR.cov.45.AD.women.cl.25.40[1], med.MAR.cov.50.AD.women.cl.25.40[1], 
                                          med.MAR.cov.55.AD.women.cl.25.40[1], med.MAR.cov.60.AD.women.cl.25.40[1], 
                                          med.MAR.cov.65.AD.women.cl.25.40[1], med.MAR.cov.70.AD.women.cl.25.40[1], 
                                          med.MAR.cov.75.AD.women.cl.25.40[1], med.MAR.cov.80.AD.women.cl.25.40[1], 
                                          med.MAR.cov.85.AD.women.cl.25.40[1], med.MAR.cov.90.AD.women.cl.25.40[1], 
                                          med.MAR.cov.95.AD.women.cl.25.40[1], med.AD.num.women.true.cov.100.25.40[1]),
                                    
                                    U = c(med.MAR.cov.35.AD.women.cl.25.40[3], med.MAR.cov.40.AD.women.cl.25.40[3], 
                                          med.MAR.cov.45.AD.women.cl.25.40[3], med.MAR.cov.50.AD.women.cl.25.40[3], 
                                          med.MAR.cov.55.AD.women.cl.25.40[3], med.MAR.cov.60.AD.women.cl.25.40[3], 
                                          med.MAR.cov.65.AD.women.cl.25.40[3], med.MAR.cov.70.AD.women.cl.25.40[3], 
                                          med.MAR.cov.75.AD.women.cl.25.40[3], med.MAR.cov.80.AD.women.cl.25.40[3], 
                                          med.MAR.cov.85.AD.women.cl.25.40[3], med.MAR.cov.90.AD.women.cl.25.40[3], 
                                          med.MAR.cov.95.AD.women.cl.25.40[3], med.AD.num.women.true.cov.100.25.40[3]))

med.AD.women.25.40.df$parameter <- rep("med.AD.women.25.40", nrow(med.AD.women.25.40.df)) 




# med.men.25.40-------------------- 

med.AD.men.25.40.df <- data.frame(x=c("35", "40", "45",
                                      "50", "55", "60",
                                      "65", "70", "75",
                                      "80", "85", "90",
                                      "95", "true_100"),
                                  
                                  F = c(med.MAR.cov.35.AD.men.cl.25.40[2], med.MAR.cov.40.AD.men.cl.25.40[2], 
                                        med.MAR.cov.45.AD.men.cl.25.40[2], med.MAR.cov.50.AD.men.cl.25.40[2], 
                                        med.MAR.cov.55.AD.men.cl.25.40[2], med.MAR.cov.60.AD.men.cl.25.40[2], 
                                        med.MAR.cov.65.AD.men.cl.25.40[2], med.MAR.cov.70.AD.men.cl.25.40[2], 
                                        med.MAR.cov.75.AD.men.cl.25.40[2], med.MAR.cov.80.AD.men.cl.25.40[2], 
                                        med.MAR.cov.85.AD.men.cl.25.40[2], med.MAR.cov.90.AD.men.cl.25.40[2], 
                                        med.MAR.cov.95.AD.men.cl.25.40[2], med.AD.num.men.true.cov.100.25.40[2]),
                                  
                                  L = c(med.MAR.cov.35.AD.men.cl.25.40[1], med.MAR.cov.40.AD.men.cl.25.40[1], 
                                        med.MAR.cov.45.AD.men.cl.25.40[1], med.MAR.cov.50.AD.men.cl.25.40[1], 
                                        med.MAR.cov.55.AD.men.cl.25.40[1], med.MAR.cov.60.AD.men.cl.25.40[1], 
                                        med.MAR.cov.65.AD.men.cl.25.40[1], med.MAR.cov.70.AD.men.cl.25.40[1], 
                                        med.MAR.cov.75.AD.men.cl.25.40[1], med.MAR.cov.80.AD.men.cl.25.40[1], 
                                        med.MAR.cov.85.AD.men.cl.25.40[1], med.MAR.cov.90.AD.men.cl.25.40[1], 
                                        med.MAR.cov.95.AD.men.cl.25.40[1], med.AD.num.men.true.cov.100.25.40[1]),
                                  
                                  U = c(med.MAR.cov.35.AD.men.cl.25.40[3], med.MAR.cov.40.AD.men.cl.25.40[3], 
                                        med.MAR.cov.45.AD.men.cl.25.40[3], med.MAR.cov.50.AD.men.cl.25.40[3], 
                                        med.MAR.cov.55.AD.men.cl.25.40[3], med.MAR.cov.60.AD.men.cl.25.40[3], 
                                        med.MAR.cov.65.AD.men.cl.25.40[3], med.MAR.cov.70.AD.men.cl.25.40[3], 
                                        med.MAR.cov.75.AD.men.cl.25.40[3], med.MAR.cov.80.AD.men.cl.25.40[3], 
                                        med.MAR.cov.85.AD.men.cl.25.40[3], med.MAR.cov.90.AD.men.cl.25.40[3], 
                                        med.MAR.cov.95.AD.men.cl.25.40[3], med.AD.num.men.true.cov.100.25.40[3]))


med.AD.men.25.40.df$parameter <- rep("med.AD.men.25.40", nrow(med.AD.men.25.40.df)) 





# med.women.40.50-------------------- 

med.AD.women.40.50.df <- data.frame(x=c("35", "40", "45",
                                        "50", "55", "60",
                                        "65", "70", "75",
                                        "80", "85", "90",
                                        "95", "true_100"),
                                    
                                    F = c(med.MAR.cov.35.AD.women.cl.40.50[2], med.MAR.cov.40.AD.women.cl.40.50[2], 
                                          med.MAR.cov.45.AD.women.cl.40.50[2], med.MAR.cov.50.AD.women.cl.40.50[2], 
                                          med.MAR.cov.55.AD.women.cl.40.50[2], med.MAR.cov.60.AD.women.cl.40.50[2], 
                                          med.MAR.cov.65.AD.women.cl.40.50[2], med.MAR.cov.70.AD.women.cl.40.50[2], 
                                          med.MAR.cov.75.AD.women.cl.40.50[2], med.MAR.cov.80.AD.women.cl.40.50[2], 
                                          med.MAR.cov.85.AD.women.cl.40.50[2], med.MAR.cov.90.AD.women.cl.40.50[2], 
                                          med.MAR.cov.95.AD.women.cl.40.50[2], med.AD.num.women.true.cov.100.40.50[2]),
                                    
                                    L = c(med.MAR.cov.35.AD.women.cl.40.50[1], med.MAR.cov.40.AD.women.cl.40.50[1], 
                                          med.MAR.cov.45.AD.women.cl.40.50[1], med.MAR.cov.50.AD.women.cl.40.50[1], 
                                          med.MAR.cov.55.AD.women.cl.40.50[1], med.MAR.cov.60.AD.women.cl.40.50[1], 
                                          med.MAR.cov.65.AD.women.cl.40.50[1], med.MAR.cov.70.AD.women.cl.40.50[1], 
                                          med.MAR.cov.75.AD.women.cl.40.50[1], med.MAR.cov.80.AD.women.cl.40.50[1], 
                                          med.MAR.cov.85.AD.women.cl.40.50[1], med.MAR.cov.90.AD.women.cl.40.50[1], 
                                          med.MAR.cov.95.AD.women.cl.40.50[1], med.AD.num.women.true.cov.100.40.50[1]),
                                    
                                    U = c(med.MAR.cov.35.AD.women.cl.40.50[3], med.MAR.cov.40.AD.women.cl.40.50[3], 
                                          med.MAR.cov.45.AD.women.cl.40.50[3], med.MAR.cov.50.AD.women.cl.40.50[3], 
                                          med.MAR.cov.55.AD.women.cl.40.50[3], med.MAR.cov.60.AD.women.cl.40.50[3], 
                                          med.MAR.cov.65.AD.women.cl.40.50[3], med.MAR.cov.70.AD.women.cl.40.50[3], 
                                          med.MAR.cov.75.AD.women.cl.40.50[3], med.MAR.cov.80.AD.women.cl.40.50[3], 
                                          med.MAR.cov.85.AD.women.cl.40.50[3], med.MAR.cov.90.AD.women.cl.40.50[3], 
                                          med.MAR.cov.95.AD.women.cl.40.50[3], med.AD.num.women.true.cov.100.40.50[3]))


med.AD.women.40.50.df$parameter <- rep("med.AD.women.40.50", nrow(med.AD.women.40.50.df)) 





# med.men.40.50-------------------- 

med.AD.men.40.50.df <- data.frame(x=c("35", "40", "45",
                                      "50", "55", "60",
                                      "65", "70", "75",
                                      "80", "85", "90",
                                      "95", "true_100"),
                                  
                                  F = c(med.MAR.cov.35.AD.men.cl.40.50[2], med.MAR.cov.40.AD.men.cl.40.50[2], 
                                        med.MAR.cov.45.AD.men.cl.40.50[2], med.MAR.cov.50.AD.men.cl.40.50[2], 
                                        med.MAR.cov.55.AD.men.cl.40.50[2], med.MAR.cov.60.AD.men.cl.40.50[2], 
                                        med.MAR.cov.65.AD.men.cl.40.50[2], med.MAR.cov.70.AD.men.cl.40.50[2], 
                                        med.MAR.cov.75.AD.men.cl.40.50[2], med.MAR.cov.80.AD.men.cl.40.50[2], 
                                        med.MAR.cov.85.AD.men.cl.40.50[2], med.MAR.cov.90.AD.men.cl.40.50[2], 
                                        med.MAR.cov.95.AD.men.cl.40.50[2], med.AD.num.men.true.cov.100.40.50[2]),
                                  
                                  L = c(med.MAR.cov.35.AD.men.cl.40.50[1], med.MAR.cov.40.AD.men.cl.40.50[1], 
                                        med.MAR.cov.45.AD.men.cl.40.50[1], med.MAR.cov.50.AD.men.cl.40.50[1], 
                                        med.MAR.cov.55.AD.men.cl.40.50[1], med.MAR.cov.60.AD.men.cl.40.50[1], 
                                        med.MAR.cov.65.AD.men.cl.40.50[1], med.MAR.cov.70.AD.men.cl.40.50[1], 
                                        med.MAR.cov.75.AD.men.cl.40.50[1], med.MAR.cov.80.AD.men.cl.40.50[1], 
                                        med.MAR.cov.85.AD.men.cl.40.50[1], med.MAR.cov.90.AD.men.cl.40.50[1], 
                                        med.MAR.cov.95.AD.men.cl.40.50[1], med.AD.num.men.true.cov.100.40.50[1]),
                                  
                                  U = c(med.MAR.cov.35.AD.men.cl.40.50[3], med.MAR.cov.40.AD.men.cl.40.50[3], 
                                        med.MAR.cov.45.AD.men.cl.40.50[3], med.MAR.cov.50.AD.men.cl.40.50[3], 
                                        med.MAR.cov.55.AD.men.cl.40.50[3], med.MAR.cov.60.AD.men.cl.40.50[3], 
                                        med.MAR.cov.65.AD.men.cl.40.50[3], med.MAR.cov.70.AD.men.cl.40.50[3], 
                                        med.MAR.cov.75.AD.men.cl.40.50[3], med.MAR.cov.80.AD.men.cl.40.50[3], 
                                        med.MAR.cov.85.AD.men.cl.40.50[3], med.MAR.cov.90.AD.men.cl.40.50[3], 
                                        med.MAR.cov.95.AD.men.cl.40.50[3], med.AD.num.men.true.cov.100.40.50[3]))


med.AD.men.40.50.df$parameter <- rep("med.AD.men.40.50", nrow(med.AD.men.40.50.df)) 




med.men.AD.df <- rbind(med.AD.men.15.25.df,
                       med.AD.men.25.40.df,
                       med.AD.men.40.50.df)

med.women.AD.df <- rbind(med.AD.women.15.25.df,
                         med.AD.women.25.40.df,
                         med.AD.women.40.50.df)



med.men.AD.df$gender <- rep("Males", nrow(med.men.AD.df))


med.women.AD.df$gender <- rep("Females", nrow(med.women.AD.df))

df_all_med <- rbind(med.men.AD.df, med.women.AD.df)

colnames(df_all_med) <- c("cov", "F", "L", "U", "Parameter", "f_m")

df_all_med$age_grps <- c(rep("15_24", 14), rep("25_39", 14), rep("40_49", 14), rep("15_24", 14), rep("25_39", 14), rep("40_49", 14))

plot.med.men.women.AD.df <- ggplot(df_all_med, aes(x=cov, y=F, colour=age_grps, group=Parameter)) + 
  geom_line(size=1) +
  geom_point() +
  facet_grid(.~f_m)+
  # geom_errorbar(aes(ymin=L, ymax=U), width=.1) +
  # ggtitle("Mean age difference in MCAR") +
  xlab("Sampling Coverage (%)") + ylab("Median Age Difference") # +


print(plot.med.men.women.AD.df)

ggsave(filename = "Plot_a_12_Median_Age_Difference_at_35_95_Coverage.pdf",
       plot = plot.mean.men.women.AD.df,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 30, height = 15, units = "cm")
# sd.women.15.25-------------------- 

sd.AD.women.15.25.df <- data.frame(x=c("35", "40", "45",
                                       "50", "55", "60",
                                       "65", "70", "75",
                                       "80", "85", "90",
                                       "95", "true_100"),
                                   
                                   F = c(sd.MAR.cov.35.AD.women.cl.15.25[2], sd.MAR.cov.40.AD.women.cl.15.25[2], 
                                         sd.MAR.cov.45.AD.women.cl.15.25[2], sd.MAR.cov.50.AD.women.cl.15.25[2], 
                                         sd.MAR.cov.55.AD.women.cl.15.25[2], sd.MAR.cov.60.AD.women.cl.15.25[2], 
                                         sd.MAR.cov.65.AD.women.cl.15.25[2], sd.MAR.cov.70.AD.women.cl.15.25[2], 
                                         sd.MAR.cov.75.AD.women.cl.15.25[2], sd.MAR.cov.80.AD.women.cl.15.25[2], 
                                         sd.MAR.cov.85.AD.women.cl.15.25[2], sd.MAR.cov.90.AD.women.cl.15.25[2], 
                                         sd.MAR.cov.95.AD.women.cl.15.25[2], sd.AD.num.women.true.cov.100.15.25[2]),
                                   
                                   L = c(sd.MAR.cov.35.AD.women.cl.15.25[1], sd.MAR.cov.40.AD.women.cl.15.25[1], 
                                         sd.MAR.cov.45.AD.women.cl.15.25[1], sd.MAR.cov.50.AD.women.cl.15.25[1], 
                                         sd.MAR.cov.55.AD.women.cl.15.25[1], sd.MAR.cov.60.AD.women.cl.15.25[1], 
                                         sd.MAR.cov.65.AD.women.cl.15.25[1], sd.MAR.cov.70.AD.women.cl.15.25[1], 
                                         sd.MAR.cov.75.AD.women.cl.15.25[1], sd.MAR.cov.80.AD.women.cl.15.25[1], 
                                         sd.MAR.cov.85.AD.women.cl.15.25[1], sd.MAR.cov.90.AD.women.cl.15.25[1], 
                                         sd.MAR.cov.95.AD.women.cl.15.25[1], sd.AD.num.women.true.cov.100.15.25[1]),
                                   
                                   U = c(sd.MAR.cov.35.AD.women.cl.15.25[3], sd.MAR.cov.40.AD.women.cl.15.25[3], 
                                         sd.MAR.cov.45.AD.women.cl.15.25[3], sd.MAR.cov.50.AD.women.cl.15.25[3], 
                                         sd.MAR.cov.55.AD.women.cl.15.25[3], sd.MAR.cov.60.AD.women.cl.15.25[3], 
                                         sd.MAR.cov.65.AD.women.cl.15.25[3], sd.MAR.cov.70.AD.women.cl.15.25[3], 
                                         sd.MAR.cov.75.AD.women.cl.15.25[3], sd.MAR.cov.80.AD.women.cl.15.25[3], 
                                         sd.MAR.cov.85.AD.women.cl.15.25[3], sd.MAR.cov.90.AD.women.cl.15.25[3], 
                                         sd.MAR.cov.95.AD.women.cl.15.25[3], sd.AD.num.women.true.cov.100.15.25[3]))

sd.AD.women.15.25.df$parameter <- rep("sd.AD.women.15.25", nrow(sd.AD.women.15.25.df))




# sd.men.15.25-------------------- 

sd.AD.men.15.25.df <- data.frame(x=c("35", "40", "45",
                                     "50", "55", "60",
                                     "65", "70", "75",
                                     "80", "85", "90",
                                     "95", "true_100"),
                                 
                                 F = c(sd.MAR.cov.35.AD.men.cl.15.25[2], sd.MAR.cov.40.AD.men.cl.15.25[2], 
                                       sd.MAR.cov.45.AD.men.cl.15.25[2], sd.MAR.cov.50.AD.men.cl.15.25[2], 
                                       sd.MAR.cov.55.AD.men.cl.15.25[2], sd.MAR.cov.60.AD.men.cl.15.25[2], 
                                       sd.MAR.cov.65.AD.men.cl.15.25[2], sd.MAR.cov.70.AD.men.cl.15.25[2], 
                                       sd.MAR.cov.75.AD.men.cl.15.25[2], sd.MAR.cov.80.AD.men.cl.15.25[2], 
                                       sd.MAR.cov.85.AD.men.cl.15.25[2], sd.MAR.cov.90.AD.men.cl.15.25[2], 
                                       sd.MAR.cov.95.AD.men.cl.15.25[2], sd.AD.num.men.true.cov.100.15.25[2]),
                                 
                                 L = c(sd.MAR.cov.35.AD.men.cl.15.25[1], sd.MAR.cov.40.AD.men.cl.15.25[1], 
                                       sd.MAR.cov.45.AD.men.cl.15.25[1], sd.MAR.cov.50.AD.men.cl.15.25[1], 
                                       sd.MAR.cov.55.AD.men.cl.15.25[1], sd.MAR.cov.60.AD.men.cl.15.25[1], 
                                       sd.MAR.cov.65.AD.men.cl.15.25[1], sd.MAR.cov.70.AD.men.cl.15.25[1], 
                                       sd.MAR.cov.75.AD.men.cl.15.25[1], sd.MAR.cov.80.AD.men.cl.15.25[1], 
                                       sd.MAR.cov.85.AD.men.cl.15.25[1], sd.MAR.cov.90.AD.men.cl.15.25[1], 
                                       sd.MAR.cov.95.AD.men.cl.15.25[1], sd.AD.num.men.true.cov.100.15.25[1]),
                                 
                                 U = c(sd.MAR.cov.35.AD.men.cl.15.25[3], sd.MAR.cov.40.AD.men.cl.15.25[3], 
                                       sd.MAR.cov.45.AD.men.cl.15.25[3], sd.MAR.cov.50.AD.men.cl.15.25[3], 
                                       sd.MAR.cov.55.AD.men.cl.15.25[3], sd.MAR.cov.60.AD.men.cl.15.25[3], 
                                       sd.MAR.cov.65.AD.men.cl.15.25[3], sd.MAR.cov.70.AD.men.cl.15.25[3], 
                                       sd.MAR.cov.75.AD.men.cl.15.25[3], sd.MAR.cov.80.AD.men.cl.15.25[3], 
                                       sd.MAR.cov.85.AD.men.cl.15.25[3], sd.MAR.cov.90.AD.men.cl.15.25[3], 
                                       sd.MAR.cov.95.AD.men.cl.15.25[3], sd.AD.num.men.true.cov.100.15.25[3]))

sd.AD.men.15.25.df$parameter <- rep("sd.AD.men.15.25", nrow(sd.AD.men.15.25.df))




# sd.women.25.40-------------------- 

sd.AD.women.25.40.df <- data.frame(x=c("35", "40", "45",
                                       "50", "55", "60",
                                       "65", "70", "75",
                                       "80", "85", "90",
                                       "95", "true_100"),
                                   
                                   F = c(sd.MAR.cov.35.AD.women.cl.25.40[2], sd.MAR.cov.40.AD.women.cl.25.40[2], 
                                         sd.MAR.cov.45.AD.women.cl.25.40[2], sd.MAR.cov.50.AD.women.cl.25.40[2], 
                                         sd.MAR.cov.55.AD.women.cl.25.40[2], sd.MAR.cov.60.AD.women.cl.25.40[2], 
                                         sd.MAR.cov.65.AD.women.cl.25.40[2], sd.MAR.cov.70.AD.women.cl.25.40[2], 
                                         sd.MAR.cov.75.AD.women.cl.25.40[2], sd.MAR.cov.80.AD.women.cl.25.40[2], 
                                         sd.MAR.cov.85.AD.women.cl.25.40[2], sd.MAR.cov.90.AD.women.cl.25.40[2], 
                                         sd.MAR.cov.95.AD.women.cl.25.40[2], sd.AD.num.women.true.cov.100.25.40[2]),
                                   
                                   L = c(sd.MAR.cov.35.AD.women.cl.25.40[1], sd.MAR.cov.40.AD.women.cl.25.40[1], 
                                         sd.MAR.cov.45.AD.women.cl.25.40[1], sd.MAR.cov.50.AD.women.cl.25.40[1], 
                                         sd.MAR.cov.55.AD.women.cl.25.40[1], sd.MAR.cov.60.AD.women.cl.25.40[1], 
                                         sd.MAR.cov.65.AD.women.cl.25.40[1], sd.MAR.cov.70.AD.women.cl.25.40[1], 
                                         sd.MAR.cov.75.AD.women.cl.25.40[1], sd.MAR.cov.80.AD.women.cl.25.40[1], 
                                         sd.MAR.cov.85.AD.women.cl.25.40[1], sd.MAR.cov.90.AD.women.cl.25.40[1], 
                                         sd.MAR.cov.95.AD.women.cl.25.40[1], sd.AD.num.women.true.cov.100.25.40[1]),
                                   
                                   U = c(sd.MAR.cov.35.AD.women.cl.25.40[3], sd.MAR.cov.40.AD.women.cl.25.40[3], 
                                         sd.MAR.cov.45.AD.women.cl.25.40[3], sd.MAR.cov.50.AD.women.cl.25.40[3], 
                                         sd.MAR.cov.55.AD.women.cl.25.40[3], sd.MAR.cov.60.AD.women.cl.25.40[3], 
                                         sd.MAR.cov.65.AD.women.cl.25.40[3], sd.MAR.cov.70.AD.women.cl.25.40[3], 
                                         sd.MAR.cov.75.AD.women.cl.25.40[3], sd.MAR.cov.80.AD.women.cl.25.40[3], 
                                         sd.MAR.cov.85.AD.women.cl.25.40[3], sd.MAR.cov.90.AD.women.cl.25.40[3], 
                                         sd.MAR.cov.95.AD.women.cl.25.40[3], sd.AD.num.women.true.cov.100.25.40[3]))

sd.AD.women.25.40.df$parameter <- rep("sd.AD.women.25.40", nrow(sd.AD.women.25.40.df))




# sd.men.25.40-------------------- 

sd.AD.men.25.40.df <- data.frame(x=c("35", "40", "45",
                                     "50", "55", "60",
                                     "65", "70", "75",
                                     "80", "85", "90",
                                     "95", "true_100"),
                                 
                                 F = c(sd.MAR.cov.35.AD.men.cl.25.40[2], sd.MAR.cov.40.AD.men.cl.25.40[2], 
                                       sd.MAR.cov.45.AD.men.cl.25.40[2], sd.MAR.cov.50.AD.men.cl.25.40[2], 
                                       sd.MAR.cov.55.AD.men.cl.25.40[2], sd.MAR.cov.60.AD.men.cl.25.40[2], 
                                       sd.MAR.cov.65.AD.men.cl.25.40[2], sd.MAR.cov.70.AD.men.cl.25.40[2], 
                                       sd.MAR.cov.75.AD.men.cl.25.40[2], sd.MAR.cov.80.AD.men.cl.25.40[2], 
                                       sd.MAR.cov.85.AD.men.cl.25.40[2], sd.MAR.cov.90.AD.men.cl.25.40[2], 
                                       sd.MAR.cov.95.AD.men.cl.25.40[2], sd.AD.num.men.true.cov.100.25.40[2]),
                                 
                                 L = c(sd.MAR.cov.35.AD.men.cl.25.40[1], sd.MAR.cov.40.AD.men.cl.25.40[1], 
                                       sd.MAR.cov.45.AD.men.cl.25.40[1], sd.MAR.cov.50.AD.men.cl.25.40[1], 
                                       sd.MAR.cov.55.AD.men.cl.25.40[1], sd.MAR.cov.60.AD.men.cl.25.40[1], 
                                       sd.MAR.cov.65.AD.men.cl.25.40[1], sd.MAR.cov.70.AD.men.cl.25.40[1], 
                                       sd.MAR.cov.75.AD.men.cl.25.40[1], sd.MAR.cov.80.AD.men.cl.25.40[1], 
                                       sd.MAR.cov.85.AD.men.cl.25.40[1], sd.MAR.cov.90.AD.men.cl.25.40[1], 
                                       sd.MAR.cov.95.AD.men.cl.25.40[1], sd.AD.num.men.true.cov.100.25.40[1]),
                                 
                                 U = c(sd.MAR.cov.35.AD.men.cl.25.40[3], sd.MAR.cov.40.AD.men.cl.25.40[3], 
                                       sd.MAR.cov.45.AD.men.cl.25.40[3], sd.MAR.cov.50.AD.men.cl.25.40[3], 
                                       sd.MAR.cov.55.AD.men.cl.25.40[3], sd.MAR.cov.60.AD.men.cl.25.40[3], 
                                       sd.MAR.cov.65.AD.men.cl.25.40[3], sd.MAR.cov.70.AD.men.cl.25.40[3], 
                                       sd.MAR.cov.75.AD.men.cl.25.40[3], sd.MAR.cov.80.AD.men.cl.25.40[3], 
                                       sd.MAR.cov.85.AD.men.cl.25.40[3], sd.MAR.cov.90.AD.men.cl.25.40[3], 
                                       sd.MAR.cov.95.AD.men.cl.25.40[3], sd.AD.num.men.true.cov.100.25.40[3]))

sd.AD.men.25.40.df$parameter <- rep("sd.AD.men.25.40", nrow(sd.AD.men.25.40.df))





# sd.women.40.50-------------------- 

sd.AD.women.40.50.df <- data.frame(x=c("35", "40", "45",
                                       "50", "55", "60",
                                       "65", "70", "75",
                                       "80", "85", "90",
                                       "95", "true_100"),
                                   
                                   F = c(sd.MAR.cov.35.AD.women.cl.40.50[2], sd.MAR.cov.40.AD.women.cl.40.50[2], 
                                         sd.MAR.cov.45.AD.women.cl.40.50[2], sd.MAR.cov.50.AD.women.cl.40.50[2], 
                                         sd.MAR.cov.55.AD.women.cl.40.50[2], sd.MAR.cov.60.AD.women.cl.40.50[2], 
                                         sd.MAR.cov.65.AD.women.cl.40.50[2], sd.MAR.cov.70.AD.women.cl.40.50[2], 
                                         sd.MAR.cov.75.AD.women.cl.40.50[2], sd.MAR.cov.80.AD.women.cl.40.50[2], 
                                         sd.MAR.cov.85.AD.women.cl.40.50[2], sd.MAR.cov.90.AD.women.cl.40.50[2], 
                                         sd.MAR.cov.95.AD.women.cl.40.50[2], sd.AD.num.women.true.cov.100.40.50[2]),
                                   
                                   L = c(sd.MAR.cov.35.AD.women.cl.40.50[1], sd.MAR.cov.40.AD.women.cl.40.50[1], 
                                         sd.MAR.cov.45.AD.women.cl.40.50[1], sd.MAR.cov.50.AD.women.cl.40.50[1], 
                                         sd.MAR.cov.55.AD.women.cl.40.50[1], sd.MAR.cov.60.AD.women.cl.40.50[1], 
                                         sd.MAR.cov.65.AD.women.cl.40.50[1], sd.MAR.cov.70.AD.women.cl.40.50[1], 
                                         sd.MAR.cov.75.AD.women.cl.40.50[1], sd.MAR.cov.80.AD.women.cl.40.50[1], 
                                         sd.MAR.cov.85.AD.women.cl.40.50[1], sd.MAR.cov.90.AD.women.cl.40.50[1], 
                                         sd.MAR.cov.95.AD.women.cl.40.50[1], sd.AD.num.women.true.cov.100.40.50[1]),
                                   
                                   U = c(sd.MAR.cov.35.AD.women.cl.40.50[3], sd.MAR.cov.40.AD.women.cl.40.50[3], 
                                         sd.MAR.cov.45.AD.women.cl.40.50[3], sd.MAR.cov.50.AD.women.cl.40.50[3], 
                                         sd.MAR.cov.55.AD.women.cl.40.50[3], sd.MAR.cov.60.AD.women.cl.40.50[3], 
                                         sd.MAR.cov.65.AD.women.cl.40.50[3], sd.MAR.cov.70.AD.women.cl.40.50[3], 
                                         sd.MAR.cov.75.AD.women.cl.40.50[3], sd.MAR.cov.80.AD.women.cl.40.50[3], 
                                         sd.MAR.cov.85.AD.women.cl.40.50[3], sd.MAR.cov.90.AD.women.cl.40.50[3], 
                                         sd.MAR.cov.95.AD.women.cl.40.50[3], sd.AD.num.women.true.cov.100.40.50[3]))

sd.AD.women.40.50.df$parameter <- rep("sd.AD.women.40.50", nrow(sd.AD.women.40.50.df))




# sd.men.40.50-------------------- 

sd.AD.men.40.50.df <- data.frame(x=c("35", "40", "45",
                                     "50", "55", "60",
                                     "65", "70", "75",
                                     "80", "85", "90",
                                     "95", "true_100"),
                                 
                                 F = c(sd.MAR.cov.35.AD.men.cl.40.50[2], sd.MAR.cov.40.AD.men.cl.40.50[2], 
                                       sd.MAR.cov.45.AD.men.cl.40.50[2], sd.MAR.cov.50.AD.men.cl.40.50[2], 
                                       sd.MAR.cov.55.AD.men.cl.40.50[2], sd.MAR.cov.60.AD.men.cl.40.50[2], 
                                       sd.MAR.cov.65.AD.men.cl.40.50[2], sd.MAR.cov.70.AD.men.cl.40.50[2], 
                                       sd.MAR.cov.75.AD.men.cl.40.50[2], sd.MAR.cov.80.AD.men.cl.40.50[2], 
                                       sd.MAR.cov.85.AD.men.cl.40.50[2], sd.MAR.cov.90.AD.men.cl.40.50[2], 
                                       sd.MAR.cov.95.AD.men.cl.40.50[2], sd.AD.num.men.true.cov.100.40.50[2]),
                                 
                                 L = c(sd.MAR.cov.35.AD.men.cl.40.50[1], sd.MAR.cov.40.AD.men.cl.40.50[1], 
                                       sd.MAR.cov.45.AD.men.cl.40.50[1], sd.MAR.cov.50.AD.men.cl.40.50[1], 
                                       sd.MAR.cov.55.AD.men.cl.40.50[1], sd.MAR.cov.60.AD.men.cl.40.50[1], 
                                       sd.MAR.cov.65.AD.men.cl.40.50[1], sd.MAR.cov.70.AD.men.cl.40.50[1], 
                                       sd.MAR.cov.75.AD.men.cl.40.50[1], sd.MAR.cov.80.AD.men.cl.40.50[1], 
                                       sd.MAR.cov.85.AD.men.cl.40.50[1], sd.MAR.cov.90.AD.men.cl.40.50[1], 
                                       sd.MAR.cov.95.AD.men.cl.40.50[1], sd.AD.num.men.true.cov.100.40.50[1]),
                                 
                                 U = c(sd.MAR.cov.35.AD.men.cl.40.50[3], sd.MAR.cov.40.AD.men.cl.40.50[3], 
                                       sd.MAR.cov.45.AD.men.cl.40.50[3], sd.MAR.cov.50.AD.men.cl.40.50[3], 
                                       sd.MAR.cov.55.AD.men.cl.40.50[3], sd.MAR.cov.60.AD.men.cl.40.50[3], 
                                       sd.MAR.cov.65.AD.men.cl.40.50[3], sd.MAR.cov.70.AD.men.cl.40.50[3], 
                                       sd.MAR.cov.75.AD.men.cl.40.50[3], sd.MAR.cov.80.AD.men.cl.40.50[3], 
                                       sd.MAR.cov.85.AD.men.cl.40.50[3], sd.MAR.cov.90.AD.men.cl.40.50[3], 
                                       sd.MAR.cov.95.AD.men.cl.40.50[3], sd.AD.num.men.true.cov.100.40.50[3]))

sd.AD.men.40.50.df$parameter <- rep("sd.AD.men.40.50", nrow(sd.AD.men.40.50.df))



sd.men.AD.df <- rbind(sd.AD.men.15.25.df,
                      sd.AD.men.25.40.df,
                      sd.AD.men.40.50.df)

sd.women.AD.df <- rbind(sd.AD.women.15.25.df,
                        sd.AD.women.25.40.df,
                        sd.AD.women.40.50.df)


#

sd.men.AD.df$gender <- rep("Males", nrow(sd.men.AD.df))


sd.women.AD.df$gender <- rep("Females", nrow(sd.women.AD.df))

df_all_sd <- rbind(sd.men.AD.df, sd.women.AD.df)

colnames(df_all_sd) <- c("cov", "F", "L", "U", "Parameter", "f_m")

df_all_sd$age_grps <- c(rep("15_24", 14), rep("25_39", 14), rep("40_49", 14), rep("15_24", 14), rep("25_39", 14), rep("40_49", 14))


saveRDS(df_all_sd, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/MAR_a_13_Standard_Deviation_Age_Difference_at_35_95_Coverage.RDS")


plot.sd.men.women.AD.df <- ggplot(df_all_sd, aes(x=cov, y=F, colour=age_grps, group=Parameter)) + 
  geom_line(size=1)+
  geom_point() +
  facet_grid(.~f_m)+
  # geom_errorbar(aes(ymin=L, ymax=U), width=.1) +
  # ggtitle("Mean age difference in MCAR") +
  xlab("Sampling Coverage (%)") + ylab("Standard Deviation of Age Difference") # +


print(plot.sd.men.women.AD.df)

ggsave(filename = "Plot_a_13_Standard_Deviation_Age_Difference_at_35_95_Coverage.pdf",
       plot = plot.sd.men.women.AD.df,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 30, height = 15, units = "cm")



#

pdf("/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Plot_a_14_Mean_SD_Age_Difference_at_35_95_Coverage.pdf",
    width=15, height=15)
gridExtra::grid.arrange(plot.mean.men.women.AD.df, plot.sd.men.women.AD.df)
dev.off()
## png 
##   2

5.3 Goodness of fit of statistics of age difference in pairings from transmission clusters

We compute the Root Mean Squared Error (RMSE) of the mean/median/standard deviation of the average age difference in pairings per each sampling scenario:

\[\sqrt{mean[(V_{true_{100}} – V_{cov})^2]}\]

where \(V_{true_{100}}\) is a vector of true values of mean/median/standard deviation of the average age difference in pairings at 100%, and \(V_{cov}\) the average age difference in pairings values at a given sampling scenario.

# Cov 35

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.35.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.35.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.35.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.35.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.35.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.35.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.35 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.35.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.35.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.35.mean)

error.infer.clust.cov.100.women.15.25.cov.35.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.35.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.35.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.35.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.35.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.35.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.35.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.35.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.35.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.35.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.35.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.35.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.35.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.35.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.35.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.35.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.35 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.35.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.35.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.35.med)


error.infer.clust.cov.100.women.15.25.cov.35.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.35.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.35.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.35.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.35.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.35.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.35.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.35.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.35.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.35.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.35.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.35.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.35.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.35.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.35.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.35.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.35 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.35.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.35.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.35.sd)


error.infer.clust.cov.100.women.15.25.cov.35.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.35.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.35.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.35.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.35.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.35.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.35.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.35.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.35.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.35.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.35.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.35.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.35.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.35.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.35.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.35.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.35 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.35.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.35.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.35.mean)


error.infer.clust.cov.100.women.25.40.cov.35.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.35.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.35.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.35.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.35.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.35.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.35.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.35.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.35.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.35.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.35.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.35.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.35.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.35.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.35.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.35.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.35 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.35.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.35.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.35.med)


error.infer.clust.cov.100.women.25.40.cov.35.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.35.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.35.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.35.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.35.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.35.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.35.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.35.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.35.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.35.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.35.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.35.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.35.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.35.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.35.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.35.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.35 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.35.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.35.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.35.sd)



error.infer.clust.cov.100.women.25.40.cov.35.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.35.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.35.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.35.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.35.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.35.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.35.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.35.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.35.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.35.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.35.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.35.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.35.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.35.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.35.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.35.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.35 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.35.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.35.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.35.mean)



error.infer.clust.cov.100.women.40.50.cov.35.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.35.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.35.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.35.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.35.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.35.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.35.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.35.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.35.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.35.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.35.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.35.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.35.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.35.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.35.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.35.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.35 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.35.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.35.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.35.med)



error.infer.clust.cov.100.women.40.50.cov.35.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.35.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.35.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.35.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.35.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.35.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.35.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.35.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.35.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.35.med)

# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.35.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.35.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.35.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.35.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.35.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.35.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.35 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.35.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.35.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.35.sd)



error.infer.clust.cov.100.women.40.50.cov.35.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.35.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.35.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.35.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.35.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.35.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.35.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.35.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.35.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.35.sd)


# Cov 40

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.40.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.40.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.40.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.40.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.40.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.40.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.40 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.40.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.40.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.40.mean)


error.infer.clust.cov.100.women.15.25.cov.40.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.40.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.40.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.40.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.40.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.40.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.40.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.40.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.40.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.40.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.40.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.40.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.40.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.40.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.40.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.40.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.40 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.40.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.40.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.40.med)


error.infer.clust.cov.100.women.15.25.cov.40.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.40.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.40.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.40.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.40.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.40.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.40.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.40.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.40.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.40.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.40.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.40.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.40.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.40.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.40.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.40.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.40 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.40.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.40.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.40.sd)


error.infer.clust.cov.100.women.15.25.cov.40.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.40.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.40.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.40.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.40.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.40.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.40.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.40.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.40.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.40.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.40.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.40.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.40.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.40.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.40.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.40.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.40 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.40.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.40.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.40.mean)


error.infer.clust.cov.100.women.25.40.cov.40.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.40.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.40.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.40.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.40.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.40.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.40.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.40.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.40.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.40.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.40.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.40.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.40.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.40.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.40.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.40.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.40 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.40.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.40.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.40.med)


error.infer.clust.cov.100.women.25.40.cov.40.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.40.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.40.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.40.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.40.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.40.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.40.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.40.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.40.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.40.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.40.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.40.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.40.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.40.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.40.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.40.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.40 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.40.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.40.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.40.sd)


error.infer.clust.cov.100.women.25.40.cov.40.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.40.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.40.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.40.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.40.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.40.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.40.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.40.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.40.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.40.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.40.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.40.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.40.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.40.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.40.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.40.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.40 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.40.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.40.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.40.mean)


error.infer.clust.cov.100.women.40.50.cov.40.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.40.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.40.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.40.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.40.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.40.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.40.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.40.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.40.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.40.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.40.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.40.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.40.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.40.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.40.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.40.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.40 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.40.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.40.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.40.med)


error.infer.clust.cov.100.women.40.50.cov.40.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.40.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.40.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.40.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.40.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.40.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.40.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.40.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.40.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.40.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.40.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.40.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.40.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.40.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.40.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.40.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.40 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.40.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.40.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.40.sd)


error.infer.clust.cov.100.women.40.50.cov.40.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.40.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.40.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.40.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.40.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.40.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.40.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.40.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.40.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.40.sd)


# Cov 45

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.45.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.45.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.45.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.45.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.45.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.45.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.45 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.45.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.45.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.45.mean)


error.infer.clust.cov.100.women.15.25.cov.45.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.45.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.45.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.45.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.45.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.45.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.45.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.45.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.45.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.45.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.45.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.45.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.45.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.45.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.45.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.45.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.45 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.45.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.45.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.45.med)


error.infer.clust.cov.100.women.15.25.cov.45.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.45.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.45.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.45.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.45.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.45.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.45.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.45.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.45.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.45.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.45.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.45.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.45.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.45.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.45.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.45.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.45 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.45.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.45.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.45.sd)


error.infer.clust.cov.100.women.15.25.cov.45.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.45.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.45.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.45.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.45.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.45.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.45.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.45.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.45.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.45.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.45.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.45.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.45.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.45.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.45.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.45.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.45 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.45.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.45.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.45.mean)


error.infer.clust.cov.100.women.25.40.cov.45.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.45.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.45.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.45.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.45.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.45.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.45.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.45.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.45.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.45.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.45.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.45.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.45.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.45.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.45.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.45.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.45 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.45.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.45.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.45.med)


error.infer.clust.cov.100.women.25.40.cov.45.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.45.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.45.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.45.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.45.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.45.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.45.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.45.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.45.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.45.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.45.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.45.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.45.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.45.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.45.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.45.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.45 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.45.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.45.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.45.sd)


error.infer.clust.cov.100.women.25.40.cov.45.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.45.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.45.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.45.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.45.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.45.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.45.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.45.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.45.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.45.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.45.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.45.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.45.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.45.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.45.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.45.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.45 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.45.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.45.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.45.mean)


error.infer.clust.cov.100.women.40.50.cov.45.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.45.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.45.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.45.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.45.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.45.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.45.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.45.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.45.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.45.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.45.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.45.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.45.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.45.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.45.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.45.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.45 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.45.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.45.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.45.med)


error.infer.clust.cov.100.women.40.50.cov.45.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.45.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.45.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.45.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.45.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.45.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.45.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.45.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.45.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.45.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.45.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.45.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.45.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.45.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.45.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.45.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.45 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.45.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.45.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.45.sd)


error.infer.clust.cov.100.women.40.50.cov.45.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.45.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.45.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.45.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.45.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.45.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.45.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.45.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.45.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.45.sd)


# Cov 50

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.50.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.50.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.50.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.50.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.50.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.50.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.50 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.50.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.50.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.50.mean)


error.infer.clust.cov.100.women.15.25.cov.50.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.50.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.50.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.50.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.50.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.50.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.50.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.50.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.50.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.50.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.50.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.50.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.50.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.50.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.50.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.50.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.50 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.50.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.50.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.50.med)


error.infer.clust.cov.100.women.15.25.cov.50.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.50.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.50.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.50.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.50.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.50.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.50.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.50.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.50.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.50.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.50.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.50.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.50.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.50.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.50.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.50.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.50 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.50.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.50.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.50.sd)


error.infer.clust.cov.100.women.15.25.cov.50.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.50.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.50.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.50.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.50.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.50.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.50.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.50.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.50.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.50.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.50.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.50.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.50.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.50.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.50.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.50.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.50 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.50.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.50.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.50.mean)


error.infer.clust.cov.100.women.25.40.cov.50.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.50.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.50.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.50.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.50.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.50.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.50.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.50.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.50.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.50.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.50.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.50.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.50.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.50.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.50.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.50.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.50 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.50.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.50.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.50.med)


error.infer.clust.cov.100.women.25.40.cov.50.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.50.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.50.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.50.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.50.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.50.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.50.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.50.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.50.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.50.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.50.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.50.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.50.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.50.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.50.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.50.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.50 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.50.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.50.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.50.sd)


error.infer.clust.cov.100.women.25.40.cov.50.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.50.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.50.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.50.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.50.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.50.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.50.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.50.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.50.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.50.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.50.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.50.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.50.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.50.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.50.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.50.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.50 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.50.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.50.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.50.mean)


error.infer.clust.cov.100.women.40.50.cov.50.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.50.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.50.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.50.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.50.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.50.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.50.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.50.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.50.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.50.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.50.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.50.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.50.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.50.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.50.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.50.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.50 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.50.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.50.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.50.med)


error.infer.clust.cov.100.women.40.50.cov.50.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.50.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.50.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.50.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.50.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.50.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.50.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.50.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.50.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.50.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.50.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.50.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.50.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.50.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.50.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.50.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.50 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.50.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.50.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.50.sd)


error.infer.clust.cov.100.women.40.50.cov.50.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.50.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.50.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.50.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.50.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.50.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.50.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.50.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.50.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.50.sd)



# Cov 55

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.55.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.55.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.55.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.55.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.55.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.55.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.55 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.55.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.55.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.55.mean)


error.infer.clust.cov.100.women.15.25.cov.55.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.55.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.55.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.55.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.55.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.55.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.55.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.55.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.55.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.55.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.55.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.55.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.55.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.55.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.55.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.55.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.55 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.55.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.55.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.55.med)


error.infer.clust.cov.100.women.15.25.cov.55.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.55.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.55.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.55.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.55.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.55.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.55.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.55.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.55.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.55.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.55.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.55.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.55.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.55.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.55.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.55.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.55 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.55.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.55.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.55.sd)


error.infer.clust.cov.100.women.15.25.cov.55.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.55.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.55.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.55.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.55.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.55.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.55.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.55.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.55.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.55.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.55.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.55.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.55.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.55.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.55.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.55.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.55 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.55.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.55.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.55.mean)


error.infer.clust.cov.100.women.25.40.cov.55.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.55.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.55.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.55.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.55.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.55.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.55.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.55.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.55.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.55.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.55.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.55.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.55.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.55.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.55.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.55.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.55 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.55.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.55.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.55.med)


error.infer.clust.cov.100.women.25.40.cov.55.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.55.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.55.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.55.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.55.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.55.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.55.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.55.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.55.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.55.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.55.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.55.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.55.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.55.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.55.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.55.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.55 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.55.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.55.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.55.sd)


error.infer.clust.cov.100.women.25.40.cov.55.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.55.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.55.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.55.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.55.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.55.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.55.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.55.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.55.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.55.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.55.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.55.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.55.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.55.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.55.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.55.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.55 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.55.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.55.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.55.mean)


error.infer.clust.cov.100.women.40.50.cov.55.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.55.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.55.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.55.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.55.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.55.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.55.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.55.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.55.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.55.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.55.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.55.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.55.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.55.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.55.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.55.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.55 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.55.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.55.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.55.med)


error.infer.clust.cov.100.women.40.50.cov.55.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.55.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.55.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.55.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.55.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.55.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.55.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.55.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.55.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.55.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.55.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.55.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.55.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.55.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.55.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.55.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.55 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.55.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.55.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.55.sd)


error.infer.clust.cov.100.women.40.50.cov.55.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.55.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.55.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.55.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.55.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.55.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.55.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.55.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.55.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.55.sd)


# Cov 60

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.60.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.60.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.60.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.60.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.60.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.60.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.60 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.60.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.60.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.60.mean)


error.infer.clust.cov.100.women.15.25.cov.60.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.60.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.60.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.60.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.60.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.60.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.60.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.60.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.60.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.60.mean)

# Median
error.infer.clust.cov.100.men.15.25.cov.60.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.60.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.60.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.60.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.60.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.60.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.60 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.60.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.60.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.60.med)


error.infer.clust.cov.100.women.15.25.cov.60.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.60.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.60.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.60.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.60.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.60.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.60.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.60.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.60.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.60.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.60.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.60.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.60.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.60.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.60.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.60.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.60 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.60.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.60.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.60.sd)


error.infer.clust.cov.100.women.15.25.cov.60.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.60.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.60.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.60.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.60.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.60.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.60.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.60.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.60.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.60.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.60.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.60.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.60.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.60.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.60.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.60.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.60 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.60.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.60.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.60.mean)


error.infer.clust.cov.100.women.25.40.cov.60.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.60.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.60.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.60.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.60.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.60.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.60.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.60.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.60.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.60.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.60.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.60.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.60.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.60.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.60.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.60.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.60 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.60.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.60.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.60.med)


error.infer.clust.cov.100.women.25.40.cov.60.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.60.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.60.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.60.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.60.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.60.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.60.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.60.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.60.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.60.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.60.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.60.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.60.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.60.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.60.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.60.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.60 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.60.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.60.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.60.sd)


error.infer.clust.cov.100.women.25.40.cov.60.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.60.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.60.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.60.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.60.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.60.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.60.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.60.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.60.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.60.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.60.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.60.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.60.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.60.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.60.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.60.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.60 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.60.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.60.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.60.mean)


error.infer.clust.cov.100.women.40.50.cov.60.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.60.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.60.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.60.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.60.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.60.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.60.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.60.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.60.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.60.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.60.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.60.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.60.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.60.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.60.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.60.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.60 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.60.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.60.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.60.med)


error.infer.clust.cov.100.women.40.50.cov.60.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.60.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.60.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.60.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.60.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.60.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.60.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.60.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.60.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.60.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.60.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.60.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.60.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.60.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.60.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.60.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.60 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.60.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.60.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.60.sd)


error.infer.clust.cov.100.women.40.50.cov.60.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.60.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.60.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.60.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.60.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.60.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.60.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.60.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.60.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.60.sd)




# Cov 65

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.65.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.65.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.65.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.65.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.65.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.65.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.65 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.65.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.65.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.65.mean)



error.infer.clust.cov.100.women.15.25.cov.65.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.65.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.65.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.65.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.65.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.65.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.65.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.65.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.65.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.65.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.65.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.65.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.65.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.65.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.65.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.65.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.65 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.65.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.65.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.65.med)


error.infer.clust.cov.100.women.15.25.cov.65.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.65.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.65.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.65.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.65.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.65.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.65.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.65.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.65.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.65.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.65.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.65.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.65.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.65.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.65.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.65.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.65 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.65.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.65.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.65.sd)


error.infer.clust.cov.100.women.15.25.cov.65.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.65.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.65.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.65.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.65.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.65.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.65.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.65.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.65.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.65.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.65.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.65.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.65.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.65.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.65.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.65.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.65 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.65.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.65.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.65.mean)


error.infer.clust.cov.100.women.25.40.cov.65.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.65.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.65.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.65.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.65.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.65.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.65.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.65.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.65.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.65.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.65.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.65.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.65.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.65.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.65.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.65.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.65 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.65.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.65.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.65.med)


error.infer.clust.cov.100.women.25.40.cov.65.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.65.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.65.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.65.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.65.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.65.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.65.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.65.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.65.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.65.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.65.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.65.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.65.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.65.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.65.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.65.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.65 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.65.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.65.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.65.sd)


error.infer.clust.cov.100.women.25.40.cov.65.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.65.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.65.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.65.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.65.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.65.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.65.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.65.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.65.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.65.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.65.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.65.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.65.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.65.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.65.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.65.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.65 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.65.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.65.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.65.mean)


error.infer.clust.cov.100.women.40.50.cov.65.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.65.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.65.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.65.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.65.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.65.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.65.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.65.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.65.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.65.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.65.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.65.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.65.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.65.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.65.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.65.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.65 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.65.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.65.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.65.med)


error.infer.clust.cov.100.women.40.50.cov.65.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.65.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.65.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.65.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.65.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.65.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.65.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.65.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.65.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.65.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.65.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.65.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.65.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.65.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.65.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.65.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.65 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.65.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.65.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.65.sd)


error.infer.clust.cov.100.women.40.50.cov.65.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.65.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.65.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.65.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.65.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.65.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.65.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.65.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.65.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.65.sd)



# Cov 70

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.70.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.70.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.70.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.70.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.70.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.70.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.70 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.70.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.70.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.70.mean)


error.infer.clust.cov.100.women.15.25.cov.70.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.70.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.70.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.70.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.70.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.70.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.70.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.70.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.70.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.70.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.70.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.70.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.70.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.70.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.70.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.70.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.70 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.70.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.70.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.70.med)


error.infer.clust.cov.100.women.15.25.cov.70.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.70.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.70.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.70.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.70.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.70.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.70.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.70.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.70.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.70.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.70.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.70.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.70.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.70.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.70.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.70.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.70 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.70.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.70.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.70.sd)

error.infer.clust.cov.100.women.15.25.cov.70.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.70.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.70.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.70.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.70.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.70.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.70.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.70.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.70.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.70.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.70.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.70.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.70.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.70.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.70.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.70.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.70 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.70.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.70.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.70.mean)


error.infer.clust.cov.100.women.25.40.cov.70.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.70.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.70.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.70.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.70.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.70.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.70.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.70.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.70.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.70.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.70.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.70.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.70.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.70.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.70.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.70.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.70 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.70.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.70.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.70.med)


error.infer.clust.cov.100.women.25.40.cov.70.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.70.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.70.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.70.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.70.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.70.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.70.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.70.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.70.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.70.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.70.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.70.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.70.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.70.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.70.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.70.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.70 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.70.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.70.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.70.sd)


error.infer.clust.cov.100.women.25.40.cov.70.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.70.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.70.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.70.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.70.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.70.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.70.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.70.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.70.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.70.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.70.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.70.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.70.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.70.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.70.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.70.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.70 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.70.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.70.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.70.mean)



error.infer.clust.cov.100.women.40.50.cov.70.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.70.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.70.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.70.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.70.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.70.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.70.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.70.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.70.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.70.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.70.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.70.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.70.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.70.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.70.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.70.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.70 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.70.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.70.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.70.med)


error.infer.clust.cov.100.women.40.50.cov.70.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.70.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.70.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.70.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.70.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.70.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.70.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.70.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.70.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.70.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.70.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.70.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.70.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.70.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.70.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.70.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.70 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.70.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.70.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.70.sd)


error.infer.clust.cov.100.women.40.50.cov.70.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.70.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.70.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.70.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.70.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.70.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.70.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.70.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.70.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.70.sd)


# Cov 75


# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.75.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.75.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.75.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.75.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.75.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.75.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.75 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.75.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.75.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.75.mean)


error.infer.clust.cov.100.women.15.25.cov.75.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.75.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.75.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.75.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.75.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.75.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.75.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.75.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.75.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.75.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.75.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.75.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.75.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.75.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.75.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.75.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.75 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.75.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.75.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.75.med)


error.infer.clust.cov.100.women.15.25.cov.75.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.75.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.75.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.75.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.75.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.75.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.75.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.75.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.75.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.75.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.75.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.75.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.75.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.75.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.75.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.75.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.75 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.75.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.75.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.75.sd)


error.infer.clust.cov.100.women.15.25.cov.75.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.75.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.75.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.75.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.75.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.75.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.75.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.75.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.75.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.75.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.75.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.75.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.75.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.75.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.75.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.75.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.75 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.75.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.75.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.75.mean)


error.infer.clust.cov.100.women.25.40.cov.75.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.75.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.75.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.75.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.75.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.75.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.75.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.75.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.75.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.75.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.75.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.75.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.75.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.75.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.75.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.75.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.75 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.75.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.75.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.75.med)


error.infer.clust.cov.100.women.25.40.cov.75.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.75.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.75.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.75.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.75.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.75.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.75.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.75.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.75.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.75.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.75.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.75.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.75.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.75.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.75.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.75.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.75 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.75.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.75.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.75.sd)


error.infer.clust.cov.100.women.25.40.cov.75.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.75.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.75.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.75.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.75.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.75.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.75.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.75.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.75.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.75.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.75.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.75.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.75.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.75.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.75.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.75.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.75 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.75.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.75.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.75.mean)


error.infer.clust.cov.100.women.40.50.cov.75.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.75.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.75.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.75.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.75.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.75.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.75.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.75.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.75.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.75.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.75.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.75.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.75.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.75.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.75.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.75.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.75 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.75.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.75.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.75.med)


error.infer.clust.cov.100.women.40.50.cov.75.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.75.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.75.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.75.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.75.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.75.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.75.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.75.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.75.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.75.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.75.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.75.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.75.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.75.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.75.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.75.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.75 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.75.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.75.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.75.sd)


error.infer.clust.cov.100.women.40.50.cov.75.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.75.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.75.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.75.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.75.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.75.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.75.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.75.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.75.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.75.sd)


# Cov 80


# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.80.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.80.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.80.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.80.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.80.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.80.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.80 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.80.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.80.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.80.mean)


error.infer.clust.cov.100.women.15.25.cov.80.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.80.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.80.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.80.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.80.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.80.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.80.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.80.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.80.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.80.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.80.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.80.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.80.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.80.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.80.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.80.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.80 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.80.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.80.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.80.med)


error.infer.clust.cov.100.women.15.25.cov.80.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.80.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.80.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.80.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.80.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.80.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.80.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.80.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.80.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.80.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.80.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.80.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.80.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.80.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.80.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.80.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.80 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.80.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.80.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.80.sd)


error.infer.clust.cov.100.women.15.25.cov.80.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.80.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.80.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.80.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.80.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.80.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.80.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.80.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.80.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.80.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.80.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.80.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.80.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.80.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.80.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.80.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.80 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.80.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.80.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.80.mean)


error.infer.clust.cov.100.women.25.40.cov.80.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.80.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.80.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.80.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.80.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.80.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.80.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.80.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.80.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.80.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.80.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.80.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.80.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.80.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.80.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.80.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.80 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.80.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.80.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.80.med)


error.infer.clust.cov.100.women.25.40.cov.80.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.80.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.80.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.80.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.80.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.80.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.80.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.80.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.80.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.80.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.80.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.80.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.80.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.80.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.80.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.80.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.80 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.80.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.80.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.80.sd)


error.infer.clust.cov.100.women.25.40.cov.80.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.80.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.80.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.80.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.80.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.80.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.80.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.80.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.80.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.80.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.80.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.80.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.80.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.80.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.80.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.80.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.80 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.80.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.80.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.80.mean)


error.infer.clust.cov.100.women.40.50.cov.80.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.80.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.80.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.80.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.80.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.80.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.80.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.80.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.80.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.80.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.80.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.80.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.80.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.80.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.80.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.80.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.80 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.80.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.80.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.80.med)


error.infer.clust.cov.100.women.40.50.cov.80.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.80.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.80.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.80.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.80.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.80.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.80.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.80.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.80.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.80.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.80.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.80.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.80.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.80.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.80.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.80.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.80 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.80.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.80.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.80.sd)


error.infer.clust.cov.100.women.40.50.cov.80.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.80.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.80.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.80.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.80.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.80.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.80.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.80.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.80.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.80.sd)


# Cov 85

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.85.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.85.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.85.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.85.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.85.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.85.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.85 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.85.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.85.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.85.mean)


error.infer.clust.cov.100.women.15.25.cov.85.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.85.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.85.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.85.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.85.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.85.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.85.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.85.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.85.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.85.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.85.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.85.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.85.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.85.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.85.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.85.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.85 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.85.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.85.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.85.med)


error.infer.clust.cov.100.women.15.25.cov.85.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.85.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.85.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.85.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.85.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.85.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.85.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.85.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.85.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.85.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.85.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.85.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.85.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.85.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.85.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.85.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.85 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.85.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.85.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.85.sd)


error.infer.clust.cov.100.women.15.25.cov.85.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.85.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.85.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.85.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.85.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.85.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.85.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.85.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.85.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.85.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.85.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.85.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.85.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.85.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.85.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.85.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.85 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.85.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.85.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.85.mean)


error.infer.clust.cov.100.women.25.40.cov.85.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.85.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.85.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.85.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.85.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.85.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.85.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.85.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.85.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.85.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.85.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.85.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.85.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.85.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.85.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.85.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.85 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.85.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.85.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.85.med)


error.infer.clust.cov.100.women.25.40.cov.85.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.85.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.85.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.85.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.85.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.85.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.85.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.85.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.85.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.85.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.85.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.85.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.85.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.85.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.85.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.85.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.85 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.85.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.85.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.85.sd)


error.infer.clust.cov.100.women.25.40.cov.85.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.85.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.85.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.85.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.85.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.85.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.85.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.85.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.85.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.85.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.85.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.85.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.85.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.85.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.85.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.85.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.85 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.85.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.85.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.85.mean)


error.infer.clust.cov.100.women.40.50.cov.85.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.85.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.85.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.85.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.85.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.85.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.85.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.85.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.85.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.85.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.85.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.85.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.85.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.85.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.85.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.85.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.85 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.85.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.85.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.85.med)


error.infer.clust.cov.100.women.40.50.cov.85.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.85.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.85.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.85.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.85.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.85.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.85.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.85.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.85.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.85.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.85.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.85.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.85.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.85.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.85.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.85.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.85 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.85.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.85.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.85.sd)


error.infer.clust.cov.100.women.40.50.cov.85.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.85.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.85.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.85.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.85.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.85.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.85.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.85.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.85.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.85.sd)


# Cov 90

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.90.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.90.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.90.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.90.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.90.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.90.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.90 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.90.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.90.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.90.mean)


error.infer.clust.cov.100.women.15.25.cov.90.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.90.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.90.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.90.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.90.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.90.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.90.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.90.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.90.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.90.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.90.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.90.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.90.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.90.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.90.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.90.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.90 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.90.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.90.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.90.med)


error.infer.clust.cov.100.women.15.25.cov.90.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.90.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.90.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.90.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.90.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.90.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.90.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.90.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.90.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.90.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.90.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.90.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.90.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.90.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.90.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.90.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.90 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.90.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.90.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.90.sd)


error.infer.clust.cov.100.women.15.25.cov.90.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.90.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.90.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.90.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.90.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.90.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.90.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.90.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.90.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.90.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.90.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.90.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.90.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.90.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.90.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.90.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.90 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.90.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.90.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.90.mean)


error.infer.clust.cov.100.women.25.40.cov.90.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.90.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.90.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.90.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.90.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.90.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.90.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.90.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.90.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.90.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.90.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.90.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.90.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.90.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.90.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.90.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.90 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.90.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.90.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.90.med)


error.infer.clust.cov.100.women.25.40.cov.90.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.90.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.90.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.90.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.90.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.90.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.90.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.90.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.90.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.90.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.90.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.90.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.90.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.90.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.90.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.90.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.90 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.90.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.90.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.90.sd)


error.infer.clust.cov.100.women.25.40.cov.90.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.90.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.90.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.90.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.90.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.90.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.90.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.90.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.90.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.90.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.90.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.90.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.90.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.90.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.90.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.90.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.90 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.90.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.90.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.90.mean)


error.infer.clust.cov.100.women.40.50.cov.90.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.90.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.90.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.90.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.90.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.90.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.90.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.90.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.90.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.90.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.90.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.90.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.90.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.90.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.90.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.90.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.90 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.90.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.90.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.90.med)


error.infer.clust.cov.100.women.40.50.cov.90.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.90.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.90.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.90.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.90.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.90.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.90.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.90.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.90.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.90.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.90.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.90.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.90.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.90.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.90.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.90.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.90 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.90.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.90.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.90.sd)


error.infer.clust.cov.100.women.40.50.cov.90.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.90.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.90.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.90.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.90.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.90.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.90.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.90.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.90.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.90.sd)



# Cov 95

# 15.25

# Mean
error.infer.clust.cov.100.men.15.25.cov.95.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.95.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.95.mean <- RMSE(error.infer.clust.cov.100.men.15.25.cov.95.mean)
MAE.error.infer.clust.cov.100.men.15.25.cov.95.mean <- MAE(error.infer.clust.cov.100.men.15.25.cov.95.mean)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.95 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.15.25, v2=vector.mean.MAR.cov.95.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.95.mean <- MRE(error.infer.clust.cov.100.men.15.25.cov.95.mean)


error.infer.clust.cov.100.women.15.25.cov.95.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.15.25) - as.numeric(vector.mean.MAR.cov.95.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.95.mean <- RMSE(error.infer.clust.cov.100.women.15.25.cov.95.mean)
MAE.error.infer.clust.cov.100.women.15.25.cov.95.mean <- MAE(error.infer.clust.cov.100.women.15.25.cov.95.mean)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.95.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.15.25, v2=vector.mean.MAR.cov.95.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.95.mean <- MRE(error.infer.clust.cov.100.women.15.25.cov.95.mean)


# Median
error.infer.clust.cov.100.men.15.25.cov.95.med <- as.numeric(vector.med.AD.num.men.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.95.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.95.med <- RMSE(error.infer.clust.cov.100.men.15.25.cov.95.med)
MAE.error.infer.clust.cov.100.men.15.25.cov.95.med <- MAE(error.infer.clust.cov.100.men.15.25.cov.95.med)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.95 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.15.25, v2=vector.med.MAR.cov.95.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.95.med <- MRE(error.infer.clust.cov.100.men.15.25.cov.95.med)


error.infer.clust.cov.100.women.15.25.cov.95.med <- as.numeric(vector.med.AD.num.women.true.cov.100.15.25) - as.numeric(vector.med.MAR.cov.95.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.95.med <- RMSE(error.infer.clust.cov.100.women.15.25.cov.95.med)
MAE.error.infer.clust.cov.100.women.15.25.cov.95.med <- MAE(error.infer.clust.cov.100.women.15.25.cov.95.med)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.95.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.15.25, v2=vector.med.MAR.cov.95.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.95.med <- MRE(error.infer.clust.cov.100.women.15.25.cov.95.med)


# Standard deviation
error.infer.clust.cov.100.men.15.25.cov.95.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.95.AD.men.cl.15.25)
RMSE.error.infer.clust.cov.100.men.15.25.cov.95.sd <- RMSE(error.infer.clust.cov.100.men.15.25.cov.95.sd)
MAE.error.infer.clust.cov.100.men.15.25.cov.95.sd <- MAE(error.infer.clust.cov.100.men.15.25.cov.95.sd)
ARMSE.error.infer.clust.cov.100.men.15.25.cov.95 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.15.25, v2=vector.sd.MAR.cov.95.AD.men.cl.15.25)
MRE.error.infer.clust.cov.100.men.15.25.cov.95.sd <- MRE(error.infer.clust.cov.100.men.15.25.cov.95.sd)


error.infer.clust.cov.100.women.15.25.cov.95.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.15.25) - as.numeric(vector.sd.MAR.cov.95.AD.women.cl.15.25)
RMSE.error.infer.clust.cov.100.women.15.25.cov.95.sd <- RMSE(error.infer.clust.cov.100.women.15.25.cov.95.sd)
MAE.error.infer.clust.cov.100.women.15.25.cov.95.sd <- MAE(error.infer.clust.cov.100.women.15.25.cov.95.sd)
ARMSE.error.infer.clust.cov.100.women.15.25.cov.95.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.15.25, v2=vector.sd.MAR.cov.95.AD.women.cl.15.25)
MRE.error.infer.clust.cov.100.women.15.25.cov.95.sd <- MRE(error.infer.clust.cov.100.women.15.25.cov.95.sd)


# 25.40

# Mean
error.infer.clust.cov.100.men.25.40.cov.95.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.95.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.95.mean <- RMSE(error.infer.clust.cov.100.men.25.40.cov.95.mean)
MAE.error.infer.clust.cov.100.men.25.40.cov.95.mean <- MAE(error.infer.clust.cov.100.men.25.40.cov.95.mean)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.95 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.25.40, v2=vector.mean.MAR.cov.95.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.95.mean <- MRE(error.infer.clust.cov.100.men.25.40.cov.95.mean)


error.infer.clust.cov.100.women.25.40.cov.95.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.25.40) - as.numeric(vector.mean.MAR.cov.95.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.95.mean <- RMSE(error.infer.clust.cov.100.women.25.40.cov.95.mean)
MAE.error.infer.clust.cov.100.women.25.40.cov.95.mean <- MAE(error.infer.clust.cov.100.women.25.40.cov.95.mean)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.95.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.25.40, v2=vector.mean.MAR.cov.95.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.95.mean <- MRE(error.infer.clust.cov.100.women.25.40.cov.95.mean)


# Median
error.infer.clust.cov.100.men.25.40.cov.95.med <- as.numeric(vector.med.AD.num.men.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.95.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.95.med <- RMSE(error.infer.clust.cov.100.men.25.40.cov.95.med)
MAE.error.infer.clust.cov.100.men.25.40.cov.95.med <- MAE(error.infer.clust.cov.100.men.25.40.cov.95.med)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.95 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.25.40, v2=vector.med.MAR.cov.95.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.95.med <- MRE(error.infer.clust.cov.100.men.25.40.cov.95.med)


error.infer.clust.cov.100.women.25.40.cov.95.med <- as.numeric(vector.med.AD.num.women.true.cov.100.25.40) - as.numeric(vector.med.MAR.cov.95.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.95.med <- RMSE(error.infer.clust.cov.100.women.25.40.cov.95.med)
MAE.error.infer.clust.cov.100.women.25.40.cov.95.med <- MAE(error.infer.clust.cov.100.women.25.40.cov.95.med)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.95.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.25.40, v2=vector.med.MAR.cov.95.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.95.med <- MRE(error.infer.clust.cov.100.women.25.40.cov.95.med)


# Standard deviation
error.infer.clust.cov.100.men.25.40.cov.95.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.95.AD.men.cl.25.40)
RMSE.error.infer.clust.cov.100.men.25.40.cov.95.sd <- RMSE(error.infer.clust.cov.100.men.25.40.cov.95.sd)
MAE.error.infer.clust.cov.100.men.25.40.cov.95.sd <- MAE(error.infer.clust.cov.100.men.25.40.cov.95.sd)
ARMSE.error.infer.clust.cov.100.men.25.40.cov.95 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.25.40, v2=vector.sd.MAR.cov.95.AD.men.cl.25.40)
MRE.error.infer.clust.cov.100.men.25.40.cov.95.sd <- MRE(error.infer.clust.cov.100.men.25.40.cov.95.sd)


error.infer.clust.cov.100.women.25.40.cov.95.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.25.40) - as.numeric(vector.sd.MAR.cov.95.AD.women.cl.25.40)
RMSE.error.infer.clust.cov.100.women.25.40.cov.95.sd <- RMSE(error.infer.clust.cov.100.women.25.40.cov.95.sd)
MAE.error.infer.clust.cov.100.women.25.40.cov.95.sd <- MAE(error.infer.clust.cov.100.women.25.40.cov.95.sd)
ARMSE.error.infer.clust.cov.100.women.25.40.cov.95.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.25.40, v2=vector.sd.MAR.cov.95.AD.women.cl.25.40)
MRE.error.infer.clust.cov.100.women.25.40.cov.95.sd <- MRE(error.infer.clust.cov.100.women.25.40.cov.95.sd)


# 40.50

# Mean
error.infer.clust.cov.100.men.40.50.cov.95.mean <- as.numeric(vector.mean.AD.num.men.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.95.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.95.mean <- RMSE(error.infer.clust.cov.100.men.40.50.cov.95.mean)
MAE.error.infer.clust.cov.100.men.40.50.cov.95.mean <- MAE(error.infer.clust.cov.100.men.40.50.cov.95.mean)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.95 <- ARMSE(v1=vector.mean.AD.num.men.true.cov.100.40.50, v2=vector.mean.MAR.cov.95.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.95.mean <- MRE(error.infer.clust.cov.100.men.40.50.cov.95.mean)


error.infer.clust.cov.100.women.40.50.cov.95.mean <- as.numeric(vector.mean.AD.num.women.true.cov.100.40.50) - as.numeric(vector.mean.MAR.cov.95.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.95.mean <- RMSE(error.infer.clust.cov.100.women.40.50.cov.95.mean)
MAE.error.infer.clust.cov.100.women.40.50.cov.95.mean <- MAE(error.infer.clust.cov.100.women.40.50.cov.95.mean)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.95.mean <- ARMSE(v1=vector.mean.AD.num.women.true.cov.100.40.50, v2=vector.mean.MAR.cov.95.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.95.mean <- MRE(error.infer.clust.cov.100.women.40.50.cov.95.mean)


# Median
error.infer.clust.cov.100.men.40.50.cov.95.med <- as.numeric(vector.med.AD.num.men.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.95.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.95.med <- RMSE(error.infer.clust.cov.100.men.40.50.cov.95.med)
MAE.error.infer.clust.cov.100.men.40.50.cov.95.med <- MAE(error.infer.clust.cov.100.men.40.50.cov.95.med)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.95 <- ARMSE(v1=vector.med.AD.num.men.true.cov.100.40.50, v2=vector.med.MAR.cov.95.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.95.med <- MRE(error.infer.clust.cov.100.men.40.50.cov.95.med)


error.infer.clust.cov.100.women.40.50.cov.95.med <- as.numeric(vector.med.AD.num.women.true.cov.100.40.50) - as.numeric(vector.med.MAR.cov.95.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.95.med <- RMSE(error.infer.clust.cov.100.women.40.50.cov.95.med)
MAE.error.infer.clust.cov.100.women.40.50.cov.95.med <- MAE(error.infer.clust.cov.100.women.40.50.cov.95.med)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.95.med <- ARMSE(v1=vector.med.AD.num.women.true.cov.100.40.50, v2=vector.med.MAR.cov.95.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.95.med <- MRE(error.infer.clust.cov.100.women.40.50.cov.95.med)


# Standard deviation
error.infer.clust.cov.100.men.40.50.cov.95.sd <- as.numeric(vector.sd.AD.num.men.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.95.AD.men.cl.40.50)
RMSE.error.infer.clust.cov.100.men.40.50.cov.95.sd <- RMSE(error.infer.clust.cov.100.men.40.50.cov.95.sd)
MAE.error.infer.clust.cov.100.men.40.50.cov.95.sd <- MAE(error.infer.clust.cov.100.men.40.50.cov.95.sd)
ARMSE.error.infer.clust.cov.100.men.40.50.cov.95 <- ARMSE(v1=vector.sd.AD.num.men.true.cov.100.40.50, v2=vector.sd.MAR.cov.95.AD.men.cl.40.50)
MRE.error.infer.clust.cov.100.men.40.50.cov.95.sd <- MRE(error.infer.clust.cov.100.men.40.50.cov.95.sd)


error.infer.clust.cov.100.women.40.50.cov.95.sd <- as.numeric(vector.sd.AD.num.women.true.cov.100.40.50) - as.numeric(vector.sd.MAR.cov.95.AD.women.cl.40.50)
RMSE.error.infer.clust.cov.100.women.40.50.cov.95.sd <- RMSE(error.infer.clust.cov.100.women.40.50.cov.95.sd)
MAE.error.infer.clust.cov.100.women.40.50.cov.95.sd <- MAE(error.infer.clust.cov.100.women.40.50.cov.95.sd)
ARMSE.error.infer.clust.cov.100.women.40.50.cov.95.sd <- ARMSE(v1=vector.sd.AD.num.women.true.cov.100.40.50, v2=vector.sd.MAR.cov.95.AD.women.cl.40.50)
MRE.error.infer.clust.cov.100.women.40.50.cov.95.sd <- MRE(error.infer.clust.cov.100.women.40.50.cov.95.sd)
# Figures --------------------------------------


# 15 - 25

RMSE.error.infer.clust.cov.100.women.15.25.AD.mean <- data.frame(x=c("35", "40", "45",
                                                                     "50", "55", "60",
                                                                     "65", "70", "75",
                                                                     "80", "85", "90",
                                                                     "95"),
                                                                 
                                                                 F = c(RMSE.error.infer.clust.cov.100.women.15.25.cov.35.mean, RMSE.error.infer.clust.cov.100.women.15.25.cov.40.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.15.25.cov.45.mean, RMSE.error.infer.clust.cov.100.women.15.25.cov.50.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.15.25.cov.55.mean, RMSE.error.infer.clust.cov.100.women.15.25.cov.60.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.15.25.cov.65.mean, RMSE.error.infer.clust.cov.100.women.15.25.cov.70.mean, 
                                                                       RMSE.error.infer.clust.cov.100.women.15.25.cov.75.mean, RMSE.error.infer.clust.cov.100.women.15.25.cov.80.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.15.25.cov.85.mean, RMSE.error.infer.clust.cov.100.women.15.25.cov.90.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.15.25.cov.95.mean))


plot.RMSE.error.infer.clust.cov.100.women.15.25.AD.mean <- ggplot(RMSE.error.infer.clust.cov.100.women.15.25.AD.mean, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for mean age difference for women in 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.women.15.25.AD.mean
RMSE.error.infer.clust.cov.100.men.15.25.AD.mean <- data.frame(x=c("35", "40", "45",
                                                                   "50", "55", "60",
                                                                   "65", "70", "75",
                                                                   "80", "85", "90",
                                                                   "95"),
                                                               
                                                               F = c(RMSE.error.infer.clust.cov.100.men.15.25.cov.35.mean, RMSE.error.infer.clust.cov.100.men.15.25.cov.40.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.15.25.cov.45.mean, RMSE.error.infer.clust.cov.100.men.15.25.cov.50.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.15.25.cov.55.mean, RMSE.error.infer.clust.cov.100.men.15.25.cov.60.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.15.25.cov.65.mean, RMSE.error.infer.clust.cov.100.men.15.25.cov.70.mean, 
                                                                     RMSE.error.infer.clust.cov.100.men.15.25.cov.75.mean, RMSE.error.infer.clust.cov.100.men.15.25.cov.80.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.15.25.cov.85.mean, RMSE.error.infer.clust.cov.100.men.15.25.cov.90.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.15.25.cov.95.mean))


plot.RMSE.error.infer.clust.cov.100.men.15.25.AD.mean <- ggplot(RMSE.error.infer.clust.cov.100.men.15.25.AD.mean, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for mean age difference for men in 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.men.15.25.AD.mean
RMSE.error.infer.clust.cov.100.women.15.25.AD.med <- data.frame(x=c("35", "40", "45",
                                                                    "50", "55", "60",
                                                                    "65", "70", "75",
                                                                    "80", "85", "90",
                                                                    "95"),
                                                                
                                                                F = c(RMSE.error.infer.clust.cov.100.women.15.25.cov.35.med, RMSE.error.infer.clust.cov.100.women.15.25.cov.40.med,
                                                                      RMSE.error.infer.clust.cov.100.women.15.25.cov.45.med, RMSE.error.infer.clust.cov.100.women.15.25.cov.50.med,
                                                                      RMSE.error.infer.clust.cov.100.women.15.25.cov.55.med, RMSE.error.infer.clust.cov.100.women.15.25.cov.60.med,
                                                                      RMSE.error.infer.clust.cov.100.women.15.25.cov.65.med, RMSE.error.infer.clust.cov.100.women.15.25.cov.70.med, 
                                                                      RMSE.error.infer.clust.cov.100.women.15.25.cov.75.med, RMSE.error.infer.clust.cov.100.women.15.25.cov.80.med,
                                                                      RMSE.error.infer.clust.cov.100.women.15.25.cov.85.med, RMSE.error.infer.clust.cov.100.women.15.25.cov.90.med,
                                                                      RMSE.error.infer.clust.cov.100.women.15.25.cov.95.med))


plot.RMSE.error.infer.clust.cov.100.women.15.25.AD.med <- ggplot(RMSE.error.infer.clust.cov.100.women.15.25.AD.med, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for median age difference for women in 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.women.15.25.AD.med
RMSE.error.infer.clust.cov.100.men.15.25.AD.med <- data.frame(x=c("35", "40", "45",
                                                                  "50", "55", "60",
                                                                  "65", "70", "75",
                                                                  "80", "85", "90",
                                                                  "95"),
                                                              
                                                              F = c(RMSE.error.infer.clust.cov.100.men.15.25.cov.35.med, RMSE.error.infer.clust.cov.100.men.15.25.cov.40.med,
                                                                    RMSE.error.infer.clust.cov.100.men.15.25.cov.45.med, RMSE.error.infer.clust.cov.100.men.15.25.cov.50.med,
                                                                    RMSE.error.infer.clust.cov.100.men.15.25.cov.55.med, RMSE.error.infer.clust.cov.100.men.15.25.cov.60.med,
                                                                    RMSE.error.infer.clust.cov.100.men.15.25.cov.65.med, RMSE.error.infer.clust.cov.100.men.15.25.cov.70.med, 
                                                                    RMSE.error.infer.clust.cov.100.men.15.25.cov.75.med, RMSE.error.infer.clust.cov.100.men.15.25.cov.80.med,
                                                                    RMSE.error.infer.clust.cov.100.men.15.25.cov.85.med, RMSE.error.infer.clust.cov.100.men.15.25.cov.90.med,
                                                                    RMSE.error.infer.clust.cov.100.men.15.25.cov.95.med))


plot.RMSE.error.infer.clust.cov.100.men.15.25.AD.med <- ggplot(RMSE.error.infer.clust.cov.100.men.15.25.AD.med, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for median age difference for men in 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.men.15.25.AD.med
RMSE.error.infer.clust.cov.100.women.15.25.AD.sd <- data.frame(x=c("35", "40", "45",
                                                                   "50", "55", "60",
                                                                   "65", "70", "75",
                                                                   "80", "85", "90",
                                                                   "95"),
                                                               
                                                               F = c(RMSE.error.infer.clust.cov.100.women.15.25.cov.35.sd, RMSE.error.infer.clust.cov.100.women.15.25.cov.40.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.15.25.cov.45.sd, RMSE.error.infer.clust.cov.100.women.15.25.cov.50.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.15.25.cov.55.sd, RMSE.error.infer.clust.cov.100.women.15.25.cov.60.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.15.25.cov.65.sd, RMSE.error.infer.clust.cov.100.women.15.25.cov.70.sd, 
                                                                     RMSE.error.infer.clust.cov.100.women.15.25.cov.75.sd, RMSE.error.infer.clust.cov.100.women.15.25.cov.80.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.15.25.cov.85.sd, RMSE.error.infer.clust.cov.100.women.15.25.cov.90.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.15.25.cov.95.sd))


plot.RMSE.error.infer.clust.cov.100.women.15.25.AD.sd <- ggplot(RMSE.error.infer.clust.cov.100.women.15.25.AD.sd, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for SD age difference for women in 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.women.15.25.AD.sd
RMSE.error.infer.clust.cov.100.men.15.25.AD.sd <- data.frame(x=c("35", "40", "45",
                                                                 "50", "55", "60",
                                                                 "65", "70", "75",
                                                                 "80", "85", "90",
                                                                 "95"),
                                                             
                                                             F = c(RMSE.error.infer.clust.cov.100.men.15.25.cov.35.sd, RMSE.error.infer.clust.cov.100.men.15.25.cov.40.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.15.25.cov.45.sd, RMSE.error.infer.clust.cov.100.men.15.25.cov.50.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.15.25.cov.55.sd, RMSE.error.infer.clust.cov.100.men.15.25.cov.60.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.15.25.cov.65.sd, RMSE.error.infer.clust.cov.100.men.15.25.cov.70.sd, 
                                                                   RMSE.error.infer.clust.cov.100.men.15.25.cov.75.sd, RMSE.error.infer.clust.cov.100.men.15.25.cov.80.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.15.25.cov.85.sd, RMSE.error.infer.clust.cov.100.men.15.25.cov.90.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.15.25.cov.95.sd))


plot.RMSE.error.infer.clust.cov.100.men.15.25.AD.sd <- ggplot(RMSE.error.infer.clust.cov.100.men.15.25.AD.sd, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for SD age difference for men in 15 - 25 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.men.15.25.AD.sd
# 25 - 40

RMSE.error.infer.clust.cov.100.women.25.40.AD.mean <- data.frame(x=c("35", "40", "45",
                                                                     "50", "55", "60",
                                                                     "65", "70", "75",
                                                                     "80", "85", "90",
                                                                     "95"),
                                                                 
                                                                 F = c(RMSE.error.infer.clust.cov.100.women.25.40.cov.35.mean, RMSE.error.infer.clust.cov.100.women.25.40.cov.40.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.25.40.cov.45.mean, RMSE.error.infer.clust.cov.100.women.25.40.cov.50.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.25.40.cov.55.mean, RMSE.error.infer.clust.cov.100.women.25.40.cov.60.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.25.40.cov.65.mean, RMSE.error.infer.clust.cov.100.women.25.40.cov.70.mean, 
                                                                       RMSE.error.infer.clust.cov.100.women.25.40.cov.75.mean, RMSE.error.infer.clust.cov.100.women.25.40.cov.80.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.25.40.cov.85.mean, RMSE.error.infer.clust.cov.100.women.25.40.cov.90.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.25.40.cov.95.mean))


plot.RMSE.error.infer.clust.cov.100.women.25.40.AD.mean <- ggplot(RMSE.error.infer.clust.cov.100.women.25.40.AD.mean, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for mean age difference for women in 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.women.25.40.AD.mean
RMSE.error.infer.clust.cov.100.men.25.40.AD.mean <- data.frame(x=c("35", "40", "45",
                                                                   "50", "55", "60",
                                                                   "65", "70", "75",
                                                                   "80", "85", "90",
                                                                   "95"),
                                                               
                                                               F = c(RMSE.error.infer.clust.cov.100.men.25.40.cov.35.mean, RMSE.error.infer.clust.cov.100.men.25.40.cov.40.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.25.40.cov.45.mean, RMSE.error.infer.clust.cov.100.men.25.40.cov.50.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.25.40.cov.55.mean, RMSE.error.infer.clust.cov.100.men.25.40.cov.60.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.25.40.cov.65.mean, RMSE.error.infer.clust.cov.100.men.25.40.cov.70.mean, 
                                                                     RMSE.error.infer.clust.cov.100.men.25.40.cov.75.mean, RMSE.error.infer.clust.cov.100.men.25.40.cov.80.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.25.40.cov.85.mean, RMSE.error.infer.clust.cov.100.men.25.40.cov.90.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.25.40.cov.95.mean))


plot.RMSE.error.infer.clust.cov.100.men.25.40.AD.mean <- ggplot(RMSE.error.infer.clust.cov.100.men.25.40.AD.mean, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for mean age difference for men in 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.men.25.40.AD.mean
RMSE.error.infer.clust.cov.100.women.25.40.AD.med <- data.frame(x=c("35", "40", "45",
                                                                    "50", "55", "60",
                                                                    "65", "70", "75",
                                                                    "80", "85", "90",
                                                                    "95"),
                                                                
                                                                F = c(RMSE.error.infer.clust.cov.100.women.25.40.cov.35.med, RMSE.error.infer.clust.cov.100.women.25.40.cov.40.med,
                                                                      RMSE.error.infer.clust.cov.100.women.25.40.cov.45.med, RMSE.error.infer.clust.cov.100.women.25.40.cov.50.med,
                                                                      RMSE.error.infer.clust.cov.100.women.25.40.cov.55.med, RMSE.error.infer.clust.cov.100.women.25.40.cov.60.med,
                                                                      RMSE.error.infer.clust.cov.100.women.25.40.cov.65.med, RMSE.error.infer.clust.cov.100.women.25.40.cov.70.med, 
                                                                      RMSE.error.infer.clust.cov.100.women.25.40.cov.75.med, RMSE.error.infer.clust.cov.100.women.25.40.cov.80.med,
                                                                      RMSE.error.infer.clust.cov.100.women.25.40.cov.85.med, RMSE.error.infer.clust.cov.100.women.25.40.cov.90.med,
                                                                      RMSE.error.infer.clust.cov.100.women.25.40.cov.95.med))


plot.RMSE.error.infer.clust.cov.100.women.25.40.AD.med <- ggplot(RMSE.error.infer.clust.cov.100.women.25.40.AD.med, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for median age difference for women in 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.women.25.40.AD.med
RMSE.error.infer.clust.cov.100.men.25.40.AD.med <- data.frame(x=c("35", "40", "45",
                                                                  "50", "55", "60",
                                                                  "65", "70", "75",
                                                                  "80", "85", "90",
                                                                  "95"),
                                                              
                                                              F = c(RMSE.error.infer.clust.cov.100.men.25.40.cov.35.med, RMSE.error.infer.clust.cov.100.men.25.40.cov.40.med,
                                                                    RMSE.error.infer.clust.cov.100.men.25.40.cov.45.med, RMSE.error.infer.clust.cov.100.men.25.40.cov.50.med,
                                                                    RMSE.error.infer.clust.cov.100.men.25.40.cov.55.med, RMSE.error.infer.clust.cov.100.men.25.40.cov.60.med,
                                                                    RMSE.error.infer.clust.cov.100.men.25.40.cov.65.med, RMSE.error.infer.clust.cov.100.men.25.40.cov.70.med, 
                                                                    RMSE.error.infer.clust.cov.100.men.25.40.cov.75.med, RMSE.error.infer.clust.cov.100.men.25.40.cov.80.med,
                                                                    RMSE.error.infer.clust.cov.100.men.25.40.cov.85.med, RMSE.error.infer.clust.cov.100.men.25.40.cov.90.med,
                                                                    RMSE.error.infer.clust.cov.100.men.25.40.cov.95.med))


plot.RMSE.error.infer.clust.cov.100.men.25.40.AD.med <- ggplot(RMSE.error.infer.clust.cov.100.men.25.40.AD.med, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for median age difference for men in 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.men.25.40.AD.med
RMSE.error.infer.clust.cov.100.women.25.40.AD.sd <- data.frame(x=c("35", "40", "45",
                                                                   "50", "55", "60",
                                                                   "65", "70", "75",
                                                                   "80", "85", "90",
                                                                   "95"),
                                                               
                                                               F = c(RMSE.error.infer.clust.cov.100.women.25.40.cov.35.sd, RMSE.error.infer.clust.cov.100.women.25.40.cov.40.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.25.40.cov.45.sd, RMSE.error.infer.clust.cov.100.women.25.40.cov.50.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.25.40.cov.55.sd, RMSE.error.infer.clust.cov.100.women.25.40.cov.60.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.25.40.cov.65.sd, RMSE.error.infer.clust.cov.100.women.25.40.cov.70.sd, 
                                                                     RMSE.error.infer.clust.cov.100.women.25.40.cov.75.sd, RMSE.error.infer.clust.cov.100.women.25.40.cov.80.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.25.40.cov.85.sd, RMSE.error.infer.clust.cov.100.women.25.40.cov.90.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.25.40.cov.95.sd))


plot.RMSE.error.infer.clust.cov.100.women.25.40.AD.sd <- ggplot(RMSE.error.infer.clust.cov.100.women.25.40.AD.sd, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for SD age difference for women in 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.women.25.40.AD.sd
RMSE.error.infer.clust.cov.100.men.25.40.AD.sd <- data.frame(x=c("35", "40", "45",
                                                                 "50", "55", "60",
                                                                 "65", "70", "75",
                                                                 "80", "85", "90",
                                                                 "95"),
                                                             
                                                             F = c(RMSE.error.infer.clust.cov.100.men.25.40.cov.35.sd, RMSE.error.infer.clust.cov.100.men.25.40.cov.40.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.25.40.cov.45.sd, RMSE.error.infer.clust.cov.100.men.25.40.cov.50.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.25.40.cov.55.sd, RMSE.error.infer.clust.cov.100.men.25.40.cov.60.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.25.40.cov.65.sd, RMSE.error.infer.clust.cov.100.men.25.40.cov.70.sd, 
                                                                   RMSE.error.infer.clust.cov.100.men.25.40.cov.75.sd, RMSE.error.infer.clust.cov.100.men.25.40.cov.80.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.25.40.cov.85.sd, RMSE.error.infer.clust.cov.100.men.25.40.cov.90.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.25.40.cov.95.sd))


plot.RMSE.error.infer.clust.cov.100.men.25.40.AD.sd <- ggplot(RMSE.error.infer.clust.cov.100.men.25.40.AD.sd, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for SD age difference for men in 25 - 40 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.men.25.40.AD.sd
# 40 - 50

RMSE.error.infer.clust.cov.100.women.40.50.AD.mean <- data.frame(x=c("35", "40", "45",
                                                                     "50", "55", "60",
                                                                     "65", "70", "75",
                                                                     "80", "85", "90",
                                                                     "95"),
                                                                 
                                                                 F = c(RMSE.error.infer.clust.cov.100.women.40.50.cov.35.mean, RMSE.error.infer.clust.cov.100.women.40.50.cov.40.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.40.50.cov.45.mean, RMSE.error.infer.clust.cov.100.women.40.50.cov.50.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.40.50.cov.55.mean, RMSE.error.infer.clust.cov.100.women.40.50.cov.60.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.40.50.cov.65.mean, RMSE.error.infer.clust.cov.100.women.40.50.cov.70.mean, 
                                                                       RMSE.error.infer.clust.cov.100.women.40.50.cov.75.mean, RMSE.error.infer.clust.cov.100.women.40.50.cov.80.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.40.50.cov.85.mean, RMSE.error.infer.clust.cov.100.women.40.50.cov.90.mean,
                                                                       RMSE.error.infer.clust.cov.100.women.40.50.cov.95.mean))


plot.RMSE.error.infer.clust.cov.100.women.40.50.AD.mean <- ggplot(RMSE.error.infer.clust.cov.100.women.40.50.AD.mean, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for mean age difference for women in 40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.women.40.50.AD.mean
RMSE.error.infer.clust.cov.100.men.40.50.AD.mean <- data.frame(x=c("35", "40", "45",
                                                                   "50", "55", "60",
                                                                   "65", "70", "75",
                                                                   "80", "85", "90",
                                                                   "95"),
                                                               
                                                               F = c(RMSE.error.infer.clust.cov.100.men.40.50.cov.35.mean, RMSE.error.infer.clust.cov.100.men.40.50.cov.40.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.40.50.cov.45.mean, RMSE.error.infer.clust.cov.100.men.40.50.cov.50.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.40.50.cov.55.mean, RMSE.error.infer.clust.cov.100.men.40.50.cov.60.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.40.50.cov.65.mean, RMSE.error.infer.clust.cov.100.men.40.50.cov.70.mean, 
                                                                     RMSE.error.infer.clust.cov.100.men.40.50.cov.75.mean, RMSE.error.infer.clust.cov.100.men.40.50.cov.80.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.40.50.cov.85.mean, RMSE.error.infer.clust.cov.100.men.40.50.cov.90.mean,
                                                                     RMSE.error.infer.clust.cov.100.men.40.50.cov.95.mean))


plot.RMSE.error.infer.clust.cov.100.men.40.50.AD.mean <- ggplot(RMSE.error.infer.clust.cov.100.men.40.50.AD.mean, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for mean age difference for men in 40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.men.40.50.AD.mean
RMSE.error.infer.clust.cov.100.women.40.50.AD.med <- data.frame(x=c("35", "40", "45",
                                                                    "50", "55", "60",
                                                                    "65", "70", "75",
                                                                    "80", "85", "90",
                                                                    "95"),
                                                                
                                                                F = c(RMSE.error.infer.clust.cov.100.women.40.50.cov.35.med, RMSE.error.infer.clust.cov.100.women.40.50.cov.40.med,
                                                                      RMSE.error.infer.clust.cov.100.women.40.50.cov.45.med, RMSE.error.infer.clust.cov.100.women.40.50.cov.50.med,
                                                                      RMSE.error.infer.clust.cov.100.women.40.50.cov.55.med, RMSE.error.infer.clust.cov.100.women.40.50.cov.60.med,
                                                                      RMSE.error.infer.clust.cov.100.women.40.50.cov.65.med, RMSE.error.infer.clust.cov.100.women.40.50.cov.70.med, 
                                                                      RMSE.error.infer.clust.cov.100.women.40.50.cov.75.med, RMSE.error.infer.clust.cov.100.women.40.50.cov.80.med,
                                                                      RMSE.error.infer.clust.cov.100.women.40.50.cov.85.med, RMSE.error.infer.clust.cov.100.women.40.50.cov.90.med,
                                                                      RMSE.error.infer.clust.cov.100.women.40.50.cov.95.med))


plot.RMSE.error.infer.clust.cov.100.women.40.50.AD.med <- ggplot(RMSE.error.infer.clust.cov.100.women.40.50.AD.med, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for median age difference for women in 40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.women.40.50.AD.med
RMSE.error.infer.clust.cov.100.men.40.50.AD.med <- data.frame(x=c("35", "40", "45",
                                                                  "50", "55", "60",
                                                                  "65", "70", "75",
                                                                  "80", "85", "90",
                                                                  "95"),
                                                              
                                                              F = c(RMSE.error.infer.clust.cov.100.men.40.50.cov.35.med, RMSE.error.infer.clust.cov.100.men.40.50.cov.40.med,
                                                                    RMSE.error.infer.clust.cov.100.men.40.50.cov.45.med, RMSE.error.infer.clust.cov.100.men.40.50.cov.50.med,
                                                                    RMSE.error.infer.clust.cov.100.men.40.50.cov.55.med, RMSE.error.infer.clust.cov.100.men.40.50.cov.60.med,
                                                                    RMSE.error.infer.clust.cov.100.men.40.50.cov.65.med, RMSE.error.infer.clust.cov.100.men.40.50.cov.70.med, 
                                                                    RMSE.error.infer.clust.cov.100.men.40.50.cov.75.med, RMSE.error.infer.clust.cov.100.men.40.50.cov.80.med,
                                                                    RMSE.error.infer.clust.cov.100.men.40.50.cov.85.med, RMSE.error.infer.clust.cov.100.men.40.50.cov.90.med,
                                                                    RMSE.error.infer.clust.cov.100.men.40.50.cov.95.med))


plot.RMSE.error.infer.clust.cov.100.men.40.50.AD.med <- ggplot(RMSE.error.infer.clust.cov.100.men.40.50.AD.med, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for median age difference for men in 40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.men.40.50.AD.med
RMSE.error.infer.clust.cov.100.women.40.50.AD.sd <- data.frame(x=c("35", "40", "45",
                                                                   "50", "55", "60",
                                                                   "65", "70", "75",
                                                                   "80", "85", "90",
                                                                   "95"),
                                                               
                                                               F = c(RMSE.error.infer.clust.cov.100.women.40.50.cov.35.sd, RMSE.error.infer.clust.cov.100.women.40.50.cov.40.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.40.50.cov.45.sd, RMSE.error.infer.clust.cov.100.women.40.50.cov.50.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.40.50.cov.55.sd, RMSE.error.infer.clust.cov.100.women.40.50.cov.60.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.40.50.cov.65.sd, RMSE.error.infer.clust.cov.100.women.40.50.cov.70.sd, 
                                                                     RMSE.error.infer.clust.cov.100.women.40.50.cov.75.sd, RMSE.error.infer.clust.cov.100.women.40.50.cov.80.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.40.50.cov.85.sd, RMSE.error.infer.clust.cov.100.women.40.50.cov.90.sd,
                                                                     RMSE.error.infer.clust.cov.100.women.40.50.cov.95.sd))


plot.RMSE.error.infer.clust.cov.100.women.40.50.AD.sd <- ggplot(RMSE.error.infer.clust.cov.100.women.40.50.AD.sd, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for SD age difference for women in 40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")


# plot.RMSE.error.infer.clust.cov.100.women.40.50.AD.sd
RMSE.error.infer.clust.cov.100.men.40.50.AD.sd <- data.frame(x=c("35", "40", "45",
                                                                 "50", "55", "60",
                                                                 "65", "70", "75",
                                                                 "80", "85", "90",
                                                                 "95"),
                                                             
                                                             F = c(RMSE.error.infer.clust.cov.100.men.40.50.cov.35.sd, RMSE.error.infer.clust.cov.100.men.40.50.cov.40.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.40.50.cov.45.sd, RMSE.error.infer.clust.cov.100.men.40.50.cov.50.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.40.50.cov.55.sd, RMSE.error.infer.clust.cov.100.men.40.50.cov.60.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.40.50.cov.65.sd, RMSE.error.infer.clust.cov.100.men.40.50.cov.70.sd, 
                                                                   RMSE.error.infer.clust.cov.100.men.40.50.cov.75.sd, RMSE.error.infer.clust.cov.100.men.40.50.cov.80.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.40.50.cov.85.sd, RMSE.error.infer.clust.cov.100.men.40.50.cov.90.sd,
                                                                   RMSE.error.infer.clust.cov.100.men.40.50.cov.95.sd))


plot.RMSE.error.infer.clust.cov.100.men.40.50.AD.sd <- ggplot(RMSE.error.infer.clust.cov.100.men.40.50.AD.sd, aes(x = x, y = F)) +
  geom_point(size = 4) +
  ggtitle("Error for SD age difference for men in 40 - 50 - MCAR") +
  xlab("Sequence sampling coverage") + ylab("Error value")

# plot.RMSE.error.infer.clust.cov.100.men.40.50.AD.sd

5.3.1 Goodness of fit of age difference statistics for critical age groups

df_gps_mean <- rbind(RMSE.error.infer.clust.cov.100.men.15.25.AD.mean,
                     RMSE.error.infer.clust.cov.100.men.25.40.AD.mean,
                     RMSE.error.infer.clust.cov.100.men.40.50.AD.mean,
                     RMSE.error.infer.clust.cov.100.women.15.25.AD.mean,
                     RMSE.error.infer.clust.cov.100.women.25.40.AD.mean,
                     RMSE.error.infer.clust.cov.100.women.40.50.AD.mean)


d_mean <- df_gps_mean

d_mean$param <- c(rep("Males.15.25", nrow(RMSE.error.infer.clust.cov.100.men.15.25.AD.mean)),
                  rep("Males.25.40", nrow(RMSE.error.infer.clust.cov.100.men.25.40.AD.mean)),
                  rep("Males.40.50", nrow(RMSE.error.infer.clust.cov.100.men.40.50.AD.mean)),
                  
                  rep("Females.15.25", nrow(RMSE.error.infer.clust.cov.100.women.15.25.AD.mean)),
                  rep("Females.25.40", nrow(RMSE.error.infer.clust.cov.100.women.25.40.AD.mean)),
                  rep("Females.40.50", nrow(RMSE.error.infer.clust.cov.100.women.40.50.AD.mean)))


newdata_mean <- d_mean[order(d_mean$param),] 

newdata_mean$f_m <- c(rep("Females", nrow(newdata_mean)/2), rep("Males", nrow(newdata_mean)/2))

newdata_mean$age_groups <- c(rep("15_25", nrow(RMSE.error.infer.clust.cov.100.men.15.25.AD.mean)),
                             rep("25_40", nrow(RMSE.error.infer.clust.cov.100.men.25.40.AD.mean)),
                             rep("40_50", nrow(RMSE.error.infer.clust.cov.100.men.40.50.AD.mean)),
                             
                             rep("15_25", nrow(RMSE.error.infer.clust.cov.100.women.15.25.AD.mean)),
                             rep("25_40", nrow(RMSE.error.infer.clust.cov.100.women.25.40.AD.mean)),
                             rep("40_50", nrow(RMSE.error.infer.clust.cov.100.women.40.50.AD.mean)))


colnames(newdata_mean) <- c("cov", "val", "param", "f_m", "age_groups")


saveRDS(newdata_mean, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/MAR_a_15_Error_for_Mean_Age_Difference.RDS")


plot.mean.AD_seq_cov_errors_100 <- ggplot(newdata_mean, aes(x=cov, y=val, colour= age_groups, group = age_groups)) + 
  geom_line(size=1) +
  geom_point() +
  facet_grid(. ~ f_m) + 
  # theme(legend.position="top")+
  xlab("Sampling Coverage (%)") + ylab("Error value for mean of age difference")

print(plot.mean.AD_seq_cov_errors_100)

ggsave(filename = "Plot_a_15_Error_for_Mean_Age_Difference.pdf",
       plot = plot.mean.AD_seq_cov_errors_100,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 26, height = 15, units = "cm")
df_gps_sd <- rbind(RMSE.error.infer.clust.cov.100.men.15.25.AD.sd,
                     RMSE.error.infer.clust.cov.100.men.25.40.AD.sd,
                     RMSE.error.infer.clust.cov.100.men.40.50.AD.sd,
                     RMSE.error.infer.clust.cov.100.women.15.25.AD.sd,
                     RMSE.error.infer.clust.cov.100.women.25.40.AD.sd,
                     RMSE.error.infer.clust.cov.100.women.40.50.AD.sd)


d_sd <- df_gps_sd

d_sd$param <- c(rep("Males.15.25", nrow(RMSE.error.infer.clust.cov.100.men.15.25.AD.sd)),
                  rep("Males.25.40", nrow(RMSE.error.infer.clust.cov.100.men.25.40.AD.sd)),
                  rep("Males.40.50", nrow(RMSE.error.infer.clust.cov.100.men.40.50.AD.sd)),
                  
                  rep("Females.15.25", nrow(RMSE.error.infer.clust.cov.100.women.15.25.AD.sd)),
                  rep("Females.25.40", nrow(RMSE.error.infer.clust.cov.100.women.25.40.AD.sd)),
                  rep("Females.40.50", nrow(RMSE.error.infer.clust.cov.100.women.40.50.AD.sd)))


newdata_sd <- d_sd[order(d_sd$param),] 

newdata_sd$f_m <- c(rep("Females", nrow(newdata_sd)/2), rep("Males", nrow(newdata_sd)/2))

newdata_sd$age_groups <- c(rep("15_25", nrow(RMSE.error.infer.clust.cov.100.men.15.25.AD.sd)),
                             rep("25_40", nrow(RMSE.error.infer.clust.cov.100.men.25.40.AD.sd)),
                             rep("40_50", nrow(RMSE.error.infer.clust.cov.100.men.40.50.AD.sd)),
                             
                             rep("15_25", nrow(RMSE.error.infer.clust.cov.100.women.15.25.AD.sd)),
                             rep("25_40", nrow(RMSE.error.infer.clust.cov.100.women.25.40.AD.sd)),
                             rep("40_50", nrow(RMSE.error.infer.clust.cov.100.women.40.50.AD.sd)))


colnames(newdata_sd) <- c("cov", "val", "param", "f_m", "age_groups")


saveRDS(newdata_sd, file = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/MAR_a_16_Error_for_SD_Age_Difference.RDS")


plot.sd.AD_seq_cov_errors_100 <- ggplot(newdata_sd, aes(x=cov, y=val, colour= age_groups, group = age_groups)) + 
  geom_line(size=1)+
  geom_point() +
  facet_grid(. ~ f_m) + 
  # theme(legend.position="top")+
  xlab("Sampling Coverage (%)") + ylab("Error value for SD of age difference")

print(plot.sd.AD_seq_cov_errors_100)

ggsave(filename = "Plot_a_16_Error_for_SD_Age_Difference.pdf",
       plot = plot.sd.AD_seq_cov_errors_100,
       path = "/home/david/age_mixing_patterns_phylogenetic/results/MAR_a",
       width = 26, height = 15, units = "cm")



pdf("/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Plot_a_17_Error_Mean_SD_Age_Difference_at_35_95_Coverage.pdf",
    width=15, height=15)
gridExtra::grid.arrange(plot.mean.AD_seq_cov_errors_100, plot.sd.AD_seq_cov_errors_100)
dev.off()
## png 
##   2
pdf("/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Plot_a_11_15_Mean_Age_Difference_and_Error_at_35_95_Coverage.pdf",
    width=15, height=15)
gridExtra::grid.arrange(plot.mean.men.women.AD.df, plot.mean.AD_seq_cov_errors_100)
dev.off()
## png 
##   2
pdf("/home/david/age_mixing_patterns_phylogenetic/results/MAR_a/Plot_a_13_16_SD_Age_Difference_and_Error_at_35_95_Coverage.pdf",
    width=15, height=15)
gridExtra::grid.arrange(plot.sd.men.women.AD.df, plot.sd.AD_seq_cov_errors_100)
dev.off()
## png 
##   2